Providing a simulated effect of a dental treatment of a patient

文档序号：689066 发布日期：2021-04-30 浏览：14次中文

阅读说明：本技术 提供患者的牙科治疗的模拟效果 (Providing a simulated effect of a dental treatment of a patient ) 是由李英杰石超 Z·莱利克 M·A·斯托克斯 A·布什耶夫薛亚 E·P·迈耶于 2019-05-01 设计创作，主要内容包括：公开了模拟牙科治疗的系统和方法。一种方法可以包括：捕获包括患者牙齿的患者面部的第一2D图像；基于2D图像构建患者牙齿的参数化3D模型；通过渲染患者牙齿处于与牙科治疗方案的治疗目标对应的一个或多个位置和/或取向时的3D模型,产生患者牙齿的牙科治疗的模拟效果；以及根据牙科治疗方案的模拟效果来渲染具有牙齿的患者面部的第二2D图像。如本文所述,牙科治疗方案可以包括正畸因素和/或修复因素。模拟效果可以与牙科治疗方案的预估效果和/或预期效果对应。(Systems and methods of simulating dental treatment are disclosed. A method may include: capturing a first 2D image of a patient's face including the patient's teeth; constructing a parameterized 3D model of the patient's teeth based on the 2D images; generating a simulated effect of the dental treatment of the patient's teeth by rendering the 3D model of the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan; and rendering a second 2D image of the patient's face with teeth in accordance with the simulated effect of the dental treatment plan. As described herein, a dental treatment regimen may include orthodontic and/or restorative factors. The simulated effect may correspond to an estimated effect and/or an expected effect of the dental treatment regimen.)

1. A computer-implemented method of simulating orthodontic treatment, the method comprising:

capturing a first 2D image, the first 2D image including representations of a patient's face and patient's teeth;

identifying one or more shapes associated with at least one patient tooth;

constructing a parameterized 3D model of the patient's teeth based on the first 2D image using one or more case-specific parameters of the one or more shapes associated with the at least one patient's teeth;

simulating an effect of a dental treatment plan for a patient's teeth to produce a simulated effect of the dental treatment plan;

modifying the parameterized 3D model to provide a modified 3D model representing simulated effects of the dental treatment plan; and

using the modified 3D model, rendering a second 2D image representing the patient's face, the second 2D image representing the patient's teeth according to the simulated effect of the dental treatment plan.

2. The computer-implemented method of claim 1, wherein constructing the parameterized 3D model comprises:

finding edges of teeth and lips in the first 2D image;

aligning a parameterized tooth model with edges of teeth and lips in the first 2D image to determine case-specific parameters; and

storing case-specific parameters of the parameterized tooth model that align the parameterized tooth model with edges of teeth, gums, and lips in the first 2D image.

3. The computer-implemented method of claim 1, wherein rendering the second 2D image comprises:

accessing a parameterized 3D model of a patient's teeth;

projecting the positions of one or more teeth from the first 2D image onto the parameterized 3D model;

mapping color data from the 2D image to respective locations on the parametric 3D model to generate a texture of the parametric 3D model; and

using the texture as part of a second 2D image of the patient's face.

4. The computer-implemented method of claim 1, wherein the predetermined position is based on an average position of a plurality of teeth of a previous patient after the dental treatment.

5. The computer-implemented method of claim 1, wherein the predetermined position is based on an average position of a plurality of teeth of a previous patient prior to a dental treatment.

6. The computer-implemented method of claim 2, further comprising:

finding edges of teeth and lips in the first 2D image; and

aligning the parameterized 3D tooth model with edges of teeth, gums, and lips in the first 2D image.

7. The computer-implemented method of claim 1, wherein the first 2D image comprises a side image representing a side view of a patient's face.

8. The computer-implemented method of claim 1, wherein the simulated effect of the dental treatment plan comprises an estimated effect of the dental treatment plan.

9. The computer-implemented method of claim 1, wherein the simulated effect of the dental treatment plan comprises an expected effect of the dental treatment plan.

10. The computer-implemented method of claim 1, wherein the dental treatment protocol comprises an orthodontic treatment protocol, a restorative treatment protocol, or some combination of an orthodontic treatment protocol and a restorative treatment protocol.

11. The computer-implemented method of claim 1, wherein capturing the first 2D image comprises: instruct a mobile phone or camera to image the patient's face, or gather the first 2D image from a storage device or a network system.

12. The computer-implemented method of claim 1, wherein constructing a parameterized 3D model of patient teeth based on the 2D images using one or more case-specific parameters of the one or more shapes associated with the at least one patient tooth comprises:

roughly aligning teeth represented in the parametric 3D model with patient teeth represented in the 2D image; and

the desired step is performed for the first time to determine a probability that a projection of a silhouette of the 3D parametric model matches one or more edges of the 2D image.

13. The computer-implemented method of claim 12, wherein constructing a parameterized 3D model of patient teeth based on the 2D images using one or more case-specific parameters of the one or more shapes associated with the at least one patient tooth comprises:

performing a maximization step using a small angle approximation to linearize a rigid transformation of teeth in the 3D model; and

performing the expectation step a second time to determine a probability that a projection of a silhouette of the 3D parametric model matches an edge of the 2D image.

14. The computer-implemented method of claim 10, further comprising:

performing a first plurality of iterations of the desired step and the maximized step using a first subset of parameters; and

after performing the first plurality of iterations for the desired step and the maximized step with a first subset of parameters of the 3D parameterized model, performing a second plurality of iterations for the desired step and the maximized step with the first subset of parameters and a second subset of parameters.

15. A computer-implemented method of simulating orthodontic treatment, the method comprising:

capturing a first 2D image of a patient's face including the patient's teeth;

constructing a parameterized 3D model of the patient's teeth based on the first 2D image, the parameterized 3D model including case-specific parameters of the shape of at least one patient's tooth;

simulating a final orthodontic position of the patient's teeth by rendering the 3D model of the patient's teeth in the predetermined position;

rendering a second 2D image of the patient's face with the teeth in the final orthodontic position.

16. The computer-implemented method of claim 15, wherein constructing the parameterized 3D model comprises:

finding edges of teeth and lips in the first 2D image; and

aligning a parameterized tooth model with edges of teeth and lips in the first 2D image to determine case-specific parameters; and

storing case-specific parameters of the parameterized tooth model that align the parameterized tooth model with edges of teeth, gums, and lips in the first 2D image.

17. The computer-implemented method of claim 15, wherein rendering the second 2D image comprises:

rendering a parameterized model of the patient according to the positions of the teeth in the first 2D image;

projecting the 2D image onto the rendered parametric model of the patient according to the positions of the teeth in the first 2D image;

mapping color data from the 2D image to corresponding locations on the 3D model to generate a texture of the 3D model; and

rendering a second 2D image of the patient's face with the teeth in the final orthodontic position using the generated texture.

18. The computer-implemented method of claim 15, wherein rendering the second 2D image further comprises applying a simulated treatment or viewing custom options to the second 2D image.

19. The computer-implemented method of claim 18, wherein the simulated treatment or viewing customization options include one or more of changing a margin of a gum, replacing a tooth, adjusting a jaw position, or adjusting color data.

20. The computer-implemented method of claim 15, wherein the predetermined position is based on an average position of a plurality of teeth of a previous patient after orthodontic treatment.

21. The computer-implemented method of claim 15, wherein the predetermined position is based on an average position of a plurality of teeth of a previous patient prior to orthodontic treatment.

22. The computer-implemented method of claim 16, further comprising:

finding edges of teeth and lips in the first 2D image; and

aligning the parameterized 3D tooth model with edges of teeth, gums, and lips in the first 2D image.

23. The computer-implemented method of claim 15, wherein the first 2D image comprises a side image representing a side view of a patient's face.

24. A computer-implemented method of constructing a 3D model of a tooth from 2D images, the method comprising:

capturing a 2D image of a patient's face including the patient's teeth;

determining edges of teeth and gums within the first 2D image;

fitting teeth in a 3D parametric model of teeth to edges of teeth and gums within the first 2D image, the 3D parametric model including case-specific parameters of a shape of a patient's teeth; and

determining values of case-specific parameters of the 3D parameterized model based on the fits.

25. The computer-implemented method of claim 24, wherein:

fitting teeth in a 3D parameterized model of teeth to edges of teeth and gums within the first 2D image, comprising:

roughly aligning teeth in the 3D parametric model with teeth in the 2D image; and

a desired step is performed to determine a probability that a projection of a silhouette of the 3D parametric model matches an edge of the 2D image.

26. The computer-implemented method of claim 25, wherein:

fitting teeth in the 3D parameterized model of teeth to edges of teeth and gums within the first 2D image, further comprising:

performing a maximization step using a small angle approximation to linearize a rigid transformation of teeth in the model; and

the expectation step is performed again to determine a probability that a projection of a silhouette of the 3D parametric model matches an edge of the 2D image.

27. The computer-implemented method of claim 26, further comprising:

performing a first plurality of iterations of the desired step and the maximized step using a first subset of parameters; and

after performing the first plurality of iterations for the desired step and the maximized step using the first subset of parameters of the 3D parameterized model, performing a second plurality of iterations for the desired step and the maximized step using the first subset of parameters and the second subset of parameters.

28. The computer-implemented method of claim 27, wherein the number of times of the first round is the same as the number of times of the second round.

29. The computer-implemented method of claim 27, wherein the first subset of case-specific parameters of the 3D parameterized model are a scaling factor and one or more of a tooth position and a tooth orientation.

30. The computer-implemented method of claim 27, wherein the second subset of parameters of the 3D parameterized model are one or more of tooth shape and tooth position and tooth orientation.

31. A computer-implemented method of simulating orthodontic treatment, the method comprising:

constructing a 3D parameterized model of the dental arch, the 3D parameterized model including generic parameters of tooth shape, tooth position, and tooth orientation;

capturing a 2D image of a patient;

constructing a case-specific 3D parametric model of the patient's teeth from the 2D images;

determining case-specific parameters of the constructed parameterized model;

rendering a 3D parameterized model of the patient's teeth at the final simulated position; and

the rendered 3D model is inserted into a 2D image of the patient.

32. The computer-implemented method of claim 31, wherein constructing a 3D parameterized model of patient teeth from the 2D images comprises:

finding edges of teeth, gums and lips in the first 2D image; and

aligning the 3D parametric model with edges of teeth, gums and lips in the first 2D image.

33. The computer-implemented method of claim 31, further comprising:

applying a texture to the 3D parameterized model of the patient's teeth at the rendered final simulated position, wherein the texture is derived from a 2D image of the patient.

34. The computer-implemented method of claim 33, wherein the texture is derived from a 2D image of a patient by:

projecting the 2D image onto the rendered parametric model of the patient according to the positions of the teeth in the first 2D image; and

mapping color data from the 2D image to corresponding locations on the 3D model to derive a texture of the 3D model.

35. The computer-implemented method of claim 34, wherein rendering the 3D parameterized model of the patient's teeth at the final simulated position comprises:

generating an average shape of the teeth based on the 3D parameterized model;

adjusting the shape of teeth in the 3D parameterized model based on case-specific tooth shape parameters;

positioning the tooth at the average tooth position and orientation based on the average position and orientation parameters such that the tooth has a case-specific shape and average position and orientation; and

the dental arch is scaled based on case-specific arch scaling parameters.

36. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1-21.

37. A system, comprising:

a photo parameterization engine configured to generate a 3D parameterized arch model from the 2D images of the patient's face and the patient's teeth, the parameterized 3D model including case-specific parameters of the shape of at least one of the patient's teeth; and

a parameterized treatment prediction engine configured to simulate orthodontic treatment of a patient based on the 3D parameterized arch model and historical models of a plurality of patients.

38. The system of claim 37, further comprising:

a treatment projection rendering engine configured to render the 3D parameterized dental arch model.

39. The system of claim 38, wherein the photo parameterization engine, the parameterized treatment prediction engine, and the treatment projection rendering engine are together configured to perform the method of any of claims 1-21.

Technical Field

The technical field relates to digital dental technology and, more particularly, to providing simulated effects of dental (e.g., orthodontic, restorative, etc.) treatment by evaluating two-dimensional (2D) depictions of untreated teeth of a patient with reference to parameters associated with a model dental arch.

Background

Orthodontic treatment typically involves addressing the problem of tooth misalignment and/or jaw misalignment, and may include diagnosis, prevention and/or repair of malocclusions. A person seeking orthodontic treatment may seek a treatment plan to an orthodontist (e.g., a professional trained specifically from a dental college graduation). Many orthodontic treatment regimens include treatment using braces, brackets, wires, and/or polymeric appliances. An orthodontic professional designing and/or implementing the orthodontic appliance may adjust the orthodontic appliance at various times for the person seeking orthodontic treatment.

Many people practice orthodontic treatment via the introduction of dentists, other treatment professionals, or others. For example, many juvenile patients or persons with severe malocclusions may undergo orthodontic treatment via the introduction of their dentist or parent. However, many others may not know whether they should receive orthodontic treatment. For example, many people with minor malocclusions may not know whether orthodontic treatment is appropriate or desirable for them.

In addition, many people may imagine how they smile without tooth and/or jaw misalignment, for example, after undergoing an estimated and/or anticipated dental treatment, after inserting an implant or other device, where the configuration is appropriate for their face, age, tradition and/or lifestyle, etc. While it may be desirable to enable people to imagine what their smile and/or face will look like after a viable treatment regimen is completed, the computational burden and/or computational expense of existing tools makes this difficult. With the existing tools, it is also difficult for people to imagine how dental treatment has a meaningful impact on the life of the patient.

Disclosure of Invention

The present disclosure relates generally to systems, methods, and/or computer-readable media related to simulating dental treatment of a patient's teeth, and more particularly to providing a photo-realistic rendering of a two-dimensional (2D) image of a patient that represents one or more simulated (e.g., predicted and/or expected) effects of a dental treatment regimen. Embodiments herein produce a near accurate and realistic rendering of simulated effects of dental treatments and/or animations of three-dimensional (3D) models, which previously may not have been possible to generate, or can only be generated in an original manner by manual photo editing tools. As described herein, the described embodiments use automated agents and/or rules to provide simulated effects of dental (e.g., orthodontic, restorative, etc.) treatments and/or accurate and realistic rendering of animations of 3D models, which has not previously been possible. Embodiments herein enable a person considering and/or receiving orthodontic treatment to visualize on a computer a simulation of an automatically generated simulated orthodontic treatment effect and may inform someone of the choice of whether or not to seek orthodontic treatment during a general and/or specific course of orthodontic treatment. As described herein, the present disclosure also relates to systems and methods for accurately and realistically simulating a 3D model of teeth in a final orthodontic position in a 2D image of an individual.

A computer-implemented method of simulating one or more simulated effects of a dental treatment is disclosed. In some embodiments, a computer-implemented method of simulating orthodontic treatment may include capturing a first 2D image. In some embodiments, the first 2D image may include a representation of the patient's face and the patient's teeth. The method may include identifying one or more shapes associated with at least one tooth of a patient. The method may further include constructing a parameterized 3D model of the patient's teeth based on the first 2D image using one or more case-specific parameters of one or more shapes associated with at least one tooth of the patient. The method may further comprise: simulating an effect of a dental treatment plan of a patient's teeth to produce a simulated effect of the dental treatment plan; and modifying the parameterized 3D model to provide a modified 3D model representing the simulated effect of the dental treatment plan. The method may further include rendering a second 2D image representing the patient's face using the modified 3D model, wherein the second 2D image represents the patient's teeth according to the simulated effect of the dental treatment plan.

In some embodiments, constructing the parameterized 3D model comprises: finding edges of teeth and lips in the first 2D image; aligning the parameterized tooth model with edges of teeth and lips in the first 2D image to determine case-specific parameters; and storing case-specific parameters of the parameterized tooth model that align the parameterized tooth model with edges of teeth, gums, and lips in the first 2D image.

In some embodiments, rendering the second 2D image comprises: accessing a parameterized 3D model of a patient's teeth; projecting one or more tooth positions from the first 2D image onto the parametric 3D model; and mapping the color data from the 2D image to corresponding locations on the parametric 3D model to generate a texture of the parametric 3D model; and using the texture as part of a second 2D image of the patient's face.

In some embodiments, the predetermined position is based on an average position of a plurality of teeth of a previous patient after a dental treatment.

In some embodiments, the predetermined position is based on an average position of a plurality of teeth of a previous patient prior to a dental treatment.

In some embodiments, a computer-implemented method may include finding edges of teeth and lips in a first 2D image; and aligning the parameterized 3D tooth model with edges of teeth, gums, and lips in the first 2D image.

In some embodiments, the first 2D image may include a profile image representing a side view (profile) of the patient's face.

In some embodiments, the simulated effect of the dental treatment plan may include an estimated effect of the dental treatment plan.

In some embodiments, the simulated effect of the dental treatment plan may comprise an expected effect of the dental treatment plan.

In some embodiments, the dental treatment protocol may include an orthodontic treatment protocol, a restorative treatment protocol, or some combination thereof.

In some embodiments, capturing the first 2D image may include: instruct a mobile phone or camera to image the patient's face, or gather a first 2D image from a storage device or a network system.

In some embodiments, constructing a parameterized 3D model of the patient's teeth based on the 2D images using one or more case-specific parameters of one or more shapes associated with at least one tooth of the patient may comprise: coarsely aligning the teeth represented in the 3D parametric model with the patient's teeth represented in the 2D image; and performing a desired step (expecteration step) for the first time to determine a probability that a projection of a silhouette (silhouette) of the 3D parametric model matches one or more edges of the 2D image.

In some embodiments, constructing a parameterized 3D model of the patient's teeth based on the 2D images using one or more case-specific parameters of one or more shapes associated with at least one tooth of the patient may comprise: performing a maximization step (maximization step) using a small angle approximation to linearize a rigid transformation of the teeth in the 3D model; and performing the expectation step a second time to determine a probability that a projection of a silhouette of the 3D parametric model matches an edge of the 2D image.

In some embodiments, a computer-implemented method may include performing a first plurality of iterations of a desired step and a maximized step with a first subset of parameters; and performing a second plurality of iterations for the expectation step and the maximization step using the first subset of parameters and the second subset of parameters after performing the first plurality of iterations for the expectation step and the maximization step using the first subset of parameters of the 3D parameterized model.

In some embodiments, a computer-implemented method may include capturing a first 2D image of a patient's face (including the patient's teeth). The method may comprise constructing a parameterized 3D model of the patient's teeth based on the 2D images, the parameterized 3D model comprising case-specific parameters of a shape of at least one tooth of the patient. The method may further comprise: an estimated (e.g., estimated final) orthodontic position and/or an expected (e.g., expected final) orthodontic position of the patient's teeth is simulated by gathering information about one or more model dental arches (which represent smiles in the absence of tooth misalignment and/or jaw misalignment), and by rendering a 3D model of the patient's teeth when in a predetermined position (e.g., one position corresponding to the position of the teeth in the model dental arch), and rendering a second 2D image of the patient's face when the teeth are in the estimated orthodontic position.

In some embodiments, rendering the second 2D image comprises: rendering a parameterized model of the patient according to the positions of the teeth in the first 2D image; projecting the 2D image onto the rendered parametric model of the patient in accordance with the positions of the teeth in the first 2D image; and mapping the color data from the 2D image to corresponding locations on the 3D model to generate a texture of the 3D model; and rendering a second 2D image of the patient's face with the teeth in the estimated orthodontic position using the generated texture.

In some embodiments, rendering the second 2D image further comprises applying a simulated treatment or viewing custom options to the second 2D image.

In some embodiments, simulating treatment or viewing customization options may include one or more of changing the margin of the gums, replacing teeth, adjusting the jaw position, or adjusting color data.

In some embodiments, the predetermined position is based on a combination of (e.g., average) positions of a plurality of teeth of a previous patient after orthodontic treatment and/or in the absence of tooth misalignment or malocclusion.

In some embodiments, the predetermined position is based on a combination of previous (e.g., average) positions of a plurality of teeth of the patient prior to orthodontic treatment.

In some embodiments, the method may comprise: finding edges of teeth and lips in the first 2D image; and aligning the parameterized tooth model with edges of teeth, gums, and lips in the first 2D image.

In some embodiments, the first 2D image comprises a side image.

A computer-implemented method of constructing a 3D model of a tooth from 2D images is disclosed. The method can comprise the following steps: capturing a 2D image of a patient's face (including the patient's teeth); determining edges of teeth and gums within the first 2D image; fitting teeth in a 3D parametric model of the teeth to edges of teeth and gums within the first 2D image, the 3D parametric model including case-specific parameters of a shape of the patient's teeth; the values of case-specific parameters of the 3D parametric model are determined based on this fit.

In some embodiments, fitting the teeth in the 3D parameterized model of teeth to the edges of the teeth and gums within the first 2D image comprises: roughly aligning teeth in the 3D parametric model with teeth in the 2D image; and performing a desired step to determine a probability that a projection of a silhouette of the 3D parametric model matches an edge of the 2D image.

In some embodiments, fitting the teeth in the 3D parameterized model of teeth to the edges of the teeth and gums within the first 2D image further comprises: performing a maximization step using a small angle approximation to linearize a rigid transformation of the teeth in the model; and performing the expectation step again to determine a probability that the projection of the silhouette of the 3D parametric model matches the edge of the 2D image.

In some embodiments, the computer-implemented method further comprises: performing a first round of multiple iterations for the expected step and the maximized step using the first subset of parameters; and performing a second plurality of iterations for the expectation step and the maximization step using the first subset of parameters and the second subset of parameters after performing the first plurality of iterations for the expectation step and the maximization step using the first subset of parameters of the 3D parameterized model.

In some embodiments, the first round is the same as the second round multiple times.

In some embodiments, the first subset of case-specific parameters of the 3D parameterized model is one or more of a scaling factor, a tooth position, and a tooth orientation.

In some embodiments, the second subset of parameters of the 3D parameterized model is one or more of tooth shape and tooth position and tooth orientation.

A computer-implemented method of providing a simulated effect of orthodontic treatment is disclosed. The method can comprise the following steps: constructing a 3D parameterized model of the dental arch, the 3D parameterized model including general parameters of tooth shape, tooth position, and tooth orientation; capturing a 2D image of a patient; constructing a case-specific 3D parametric model of the patient's teeth from the 2D images; determining case-specific parameters of the constructed parameterized model; rendering the 3D parameterized model when the patient's teeth are in an estimated and/or expected final position (e.g., in the absence of tooth misalignment and/or malocclusion); and inserting the rendered 3D model into a 2D image of the patient.

In some embodiments, constructing a 3D parametric model of the patient's teeth from the 2D image comprises: finding edges of teeth, gums and lips in the first 2D image; and aligning the 3D parameterized model with edges of teeth, gums and lips in the first 2D image.

In some embodiments, the method further comprises applying a texture to the rendered 3D parametric model of the patient's teeth in an estimated and/or expected final position (e.g., no tooth misalignment and/or malocclusion), wherein the texture is derived from a 2D image of the patient.

In some embodiments, the texture is derived from a 2D image of the patient by: projecting the 2D image onto the rendered parametric model of the patient in accordance with the positions of the teeth in the first 2D image; and mapping color data from the 2D image to corresponding locations on the 3D model to derive a texture of the 3D model.

In some embodiments, the rendering of the 3D parametric model of the patient's teeth in the estimated and/or expected final position comprises: generating an average shape of the teeth based on the 3D parameterized model; adjusting the shape of teeth in the 3D parameterized model based on the case-specific tooth shape parameters; positioning the tooth at the average tooth position and orientation based on the average position and orientation parameters such that the tooth has a case-specific shape and average position and orientation; and scaling the dental arch based on the case-specific dental arch scaling parameters.

A non-transitory computer readable medium includes instructions that, when executed by a processor, cause the processor to perform any of the methods described herein.

A system is disclosed. The system may include: a photo parameterization engine configured to generate a 3D parameterized arch model from the 2D images of the patient's face and teeth, the parameterized 3D model including case-specific parameters of a shape of at least one tooth of the patient; and a parametric treatment prediction engine configured to identify a predicted and/or expected effect of orthodontic treatment of the patient based on the 3D parametric dental arch model and historical models and/or ideal dental arch models of the plurality of patients.

In some embodiments, the system includes a treatment projection rendering engine configured to render the 3D parameterized dental arch model.

In some embodiments, the photo parameterization engine, the parameterized treatment prediction engine, and the treatment projection rendering engine together are configured to perform the methods described herein.

Incorporation by reference

All publications, patents and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference.

Drawings

The novel features believed characteristic of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

fig. 1 illustrates a method of providing an estimated effect of orthodontic treatment according to one or more embodiments herein;

FIG. 2 illustrates a parameterized tooth model in accordance with one or more embodiments herein;

FIG. 3A illustrates an example of how well a parameterized tooth model matches an original 3D model according to one or more embodiments herein;

fig. 3B illustrates a method of determining general parameters from historical and/or ideal cases according to one or more embodiments herein;

FIG. 4 illustrates an alignment of phase cases used in determining parameters of a parameterized model according to one or more embodiments herein;

FIG. 5 illustrates a method of generating a parameterized model of a patient's teeth and converting the parameterized model to a 3D model of an arch, according to one or more embodiments herein;

FIG. 6 illustrates a method of constructing a 3D model from 2D images according to one or more embodiments herein;

FIG. 7A illustrates a method of constructing a patient-specific parameterized model of a patient's teeth according to one or more embodiments herein;

FIG. 7B illustrates a tooth model having a gingival margin and a lip margin according to one or more embodiments herein;

FIG. 8 illustrates a method of rendering a patient's teeth in an initial position using a parameterized model of the patient's dental arch according to one or more embodiments herein;

FIG. 9 illustrates a method of constructing a 3D model and applying textures to the 3D model according to one or more embodiments herein;

FIG. 10A illustrates a method of simulating an estimated effect of orthodontic treatment of a patient's teeth according to one or more embodiments herein;

fig. 10B illustrates a method of simulating orthodontic treatment of a patient based on matching tooth shape parameters according to one or more embodiments herein;

fig. 11 illustrates an example of a method of rendering teeth according to an estimated effect of a dental treatment plan according to one or more embodiments herein;

fig. 12 illustrates a system for simulating an estimated effect of orthodontic treatment according to one or more embodiments herein;

fig. 13 illustrates an example of one or more elements of an estimated orthodontic treatment simulation system according to one or more embodiments herein;

FIG. 14 illustrates a tooth repositioning appliance according to one or more embodiments herein;

FIG. 15 shows a tooth repositioning system according to one or more embodiments herein;

fig. 16 illustrates a method of orthodontic treatment with multiple appliances according to one or more embodiments herein;

fig. 17 illustrates a method of designing an orthodontic appliance according to one or more embodiments herein;

fig. 18 illustrates a method of planning an orthodontic treatment according to one or more embodiments herein;

fig. 19 is a simplified block diagram of a system for designing an orthodontic appliance and planning an orthodontic treatment according to one or more embodiments herein.

Detailed Description

The embodiments discussed herein provide tools such as automated agents to visualize the corrective effects of tooth misalignment/malocclusion, etc., without the computational burden and/or expense of scanning a patient's dentition or dentition impression, and also calculating the final position of a treatment plan for the patient's dentition, etc. As discussed in detail herein, these techniques may involve acquiring a two-dimensional (2D) representation (e.g., an image) of a patient's dentition, acquiring one or more parameters to represent attributes of the patient's dentition in the 2D representation, and comparing the attributes of the patient's dentition to attributes of a model arch using the one or more parameters, such as attributes of historical cases and/or attributes representing an ideal arch morphology. The techniques herein may provide a basis for simulating the simulated effect of a dental treatment protocol.

As used herein, "simulated effect of a dental treatment protocol" may include, for example, an estimated effect and/or an expected effect of a dental treatment protocol after performing one or more dental procedures (such as orthodontic procedures, restorative procedures, etc.). As used herein, "predicted effect of a dental treatment regimen" may include a prediction of the state of a patient's dentition after a dental procedure. In some cases, the predicted effect of a dental treatment protocol as used herein may be different from the "actual effect of a dental treatment protocol" which may represent the state of a patient's dentition after the dental treatment protocol is implemented. In various instances, the predicted and actual effects of a dental treatment regimen as used herein may be different from the "predicted effects of a dental treatment regimen" which may represent the predicted state of a patient's dentition after the dental treatment regimen is implemented. It should also be noted that the "predicted effect of the orthodontic treatment regimen" may include a prediction of the state of the patient's dentition after correction for any malposition/malocclusion, etc. of the teeth the patient is suffering from. In some embodiments, the estimated effect of the orthodontic treatment regimen includes an estimated state of the patient's dentition where the patient's dentition has changed to have a model and/or an ideal arch morphology reflected from one or more databases of historical cases and/or ideal arch morphology. "the actual effect of the orthodontic treatment protocol" may represent the state of the patient's dentition after the orthodontic treatment protocol has been applied; the "expected effect of the orthodontic treatment regimen" may represent an expected state of the patient's dentition after the orthodontic treatment regimen is administered.

The features and advantages of the present disclosure may be better understood with reference to the following detailed description and accompanying drawings that set forth illustrative embodiments, in which the principles of the embodiments of the disclosure are employed.

Fig. 1 illustrates an example of a method 100 of providing an estimated and/or expected effect of a dental treatment protocol according to one or more embodiments herein. The method 100 may be performed by any of the systems disclosed herein. It should be noted that various examples may include more or less blocks than shown in fig. 1.

In block 110, one or more two-dimensional (2D or 2-D) images of a patient are captured. In some embodiments, the 2D image shows the mouth of the patient and includes one or more images of the face, head, neck, shoulders, torso, or the entirety of the patient. The 2D images of the patient may include images of the patient with the patient's mouth in one or more positions; for example, the patient's mouth may be a smile position (e.g., a social smile position), a rest position where the muscles are relaxed and the lips are slightly open, or a retracted anterior open or closed position.

In some embodiments, an image of the patient is acquired with an image capture device. As used herein, an "image capture device" (i.e., "image capture system") may include any system capable of capturing an image. Examples of image capture devices include cameras, smart phones, digital imaging devices, components of computer systems configured to capture images, and the like. The image may be captured using a lens of a predetermined focal length at a distance from the patient. Images may also be captured remotely and then received for processing. In some embodiments, the image of the patient is obtained from a computer storage device, a network location, a social media account, or the like. The image may be a series of images or video captured from one or more angles. For example, the images may include one or more of a frontal face image and a side image, the side image including one or more three-quarters side images and a full side image.

At block 120, a three-dimensional (3D or 3-D) model of the patient's teeth is generated based on the 2D image of the patient. As discussed in more detail with reference to fig. 6 and elsewhere herein, generating the 3D model may include identifying the patient's teeth. Generating the 3D model may also include identifying the patient's gums, lips, and/or openness, forming a parameterized model of each identified tooth, and incorporating the parameterized models of teeth into one or more (e.g., two, three, etc.) parameterized models of the dental arch. As discussed further herein, the parameterized models of the patient's teeth and dental arch may be based on or referenced to the average teeth and dental arch parameterized model.

As used herein, a "parameterized model of a patient's teeth" (e.g., "parameterized model of a patient's dentition") may include a model (e.g., a statistical model) of a patient's dentition characterized by a probability distribution having a finite number of parameters. The parameterized model of the patient's dentition may include parameterized models of the patient's teeth and/or dental arches. The parametric model may include models representing objects of various dimensions, and may include a 2D parametric model, a 3D parametric model, and the like. Modeling the patient's teeth using a parameterized model of the patient's dentition may reduce memory and computational requirements (as described herein) in manipulating, comparing, or otherwise using digital models, and simplify comparisons between different models. In some embodiments, the parameterized model of the teeth may be represented as:

whereinIs the average tooth shape and is a common parameter. As described herein, each tooth (e.g., the upper right dog with tooth number 6 in the universal teeth numbering system)Teeth) have their own average tooth shape, which is calculated from thousands of teeth with the same tooth number. The symbol τ may represent an index for each tooth, which may be based on a universal tooth numbering system or other numbering system,may be a main component of the shape of each tooth, is also a general parameter, andcoefficients, which may be the principal components of the tooth shape, are case-specific parameters. Thus, equation (1) may be used to represent a parameterized model of each tooth of a particular patient (e.g., lower left incisors, upper right canines, etc.) based on the particular shape of each tooth relative to the average tooth shape of that tooth.

The parameterized model of the patient's dentition may be represented as:

whereinIs described above with reference to equation (1),is the average tooth position, is a general parameter; t is_τIs the deviation of the position of the patient's teeth from the corresponding average tooth position, is a case-specific parameter; and Φ is the arch scaling factor that scales unitless parameter values to true world values, and is also a case specific parameter. T is a global perspective of the dental arch from a perspective and is a case-specific parameter and, in some embodiments, is used only when matching with 2D images, as dental arch scans typically have no perspective, but 2D images (e.g., cameras).

In order to derive from the 3D dental model (which is derived from an image of the patient's teeth or from another known in the art)The method results) generates a parameterized 3D model of the patient's teeth, which can be modeled based on the displacement of the scanned tooth surface from a fixed shape (e.g., a fixed sphere). To illustrate this, reference is made to fig. 2, which shows a parameterized tooth model in accordance with one or more embodiments herein. In the example of fig. 2, a sphere 210 is shown having a plurality of vertices at fixed or known positions and orientations. The teeth 220 may be placed in the center of the sphere 210 or otherwise modeled on the teeth 220. In some embodiments, the center of the volume of the tooth 220, the scanned portion of the tooth 220, or the crown of the tooth 220 may be aligned with the center of the sphere 210. Each vertex 230a, 230b of the sphere 210 may then be mapped to a location on the surface of the tooth model. In some embodiments, the mapping may be represented by a matrix of n x 3, where n represents the number of points on the sphere (e.g., 2500), and then for each point, the x, y, and z positions are recorded. In some embodiments, the matrix stores the difference between the position on the average tooth and the corresponding position on the actual model. In this way, each tooth is represented by the same 2500 points, and differences between teeth are easily compared. This difference can be expressed as the PCA componentWherein each particular case ultimately has a unique profileIs gathered becauseCommon to all cases. To illustrate this, referring to fig. 3A, an example of how well a parameterized model 320 of teeth matches the original 3D model 310 is shown. The parameters for a particular tooth may be stored in a data store (e.g., database) and may be stored byAnd (4) showing.

A parameterized model of a patient's dental arch may involve parameterizing the position and orientation of each tooth in the arch and the scale of the arch. The case-specific parameters for a particular tooth may be stored in a matrix or other data store, as shown in equation (3) below:

in which ξ_ijIs a rotational component that defines the orientation of the tooth relative to the dental arch, and can be 3 rotational angles alpha of the tooth_τ、β_τ、γ_τAs a function of (c). Delta_τ,x、Δ_τ,yAnd Δ_τ,zIs the translation of the center point of the tooth relative to the origin of the dental arch. The rotation may be based on the orientation of the orthogonal axes (e.g., long axis, buccolingual axis, and mesial-distal axis) of each tooth relative to a fixed reference or relative to an average rotation. In some embodiments, one or more of these components may be expressed as a deviation or variation from the average dental arch discussed herein.

Similarly, a scaling factor Φ is applied to the teeth and arch positions of the teeth to scale the arch from a generic or unitless representation to a real world scale representing the actual size and position of the teeth in the arch. In some embodiments, the scaling may be between 3D units of projection (e.g., millimeters) and image size (e.g., pixels).

As discussed above and elsewhere herein, the parameterized model of the arch including the teeth may be represented based on an average arch model including the teeth. The average arch model may be determined based on an average of a large data set of a patient arch scan. In some embodiments, the average arch model may be based on a large dataset of parameterized arches.

For example, an average arch may be constructed from a set of previously scanned and/or segmented arches. FIG. 3B illustrates an example of a method 350 of determining an average dental arch model, and is discussed in further detail herein.

In some embodiments, the parameterized model of the dental arch may be converted to a 3D model of the dental arch. Fig. 5 illustrates a method 500 of converting a parameterized model of one or more teeth to a 3D model of an arch, according to some embodiments, and is discussed in further detail herein.

Returning to fig. 1, at block 130, the predicted and/or expected effect of the dental treatment plan on the 3D model is identified to obtain the predicted and/or expected effect of the orthodontic treatment plan. As discussed with reference to fig. 9-11, the predicted and/or expected effect of the dental treatment plan may be based on a parameterized model of the patient's teeth at the positions indicated according to a predetermined arch model. In some embodiments, the predetermined arch model may be based on an average arch model, which may be based on historical averages of arch scans or other arch models collected from the patient. In some embodiments, for example, the predetermined arch model may be based on an idealized model, e.g., based on a clinically ideal arch or arch model, wherein the tooth position and/or orientation is predetermined, e.g., based on one or more of its aesthetic and clinical characteristics (e.g., correct bite). In some embodiments, the predicted and/or expected effect of the dental treatment plan may correspond to an expected final position of the patient's teeth (or an estimated value thereof) after the treatment plan is administered. In some embodiments, the predicted and/or expected effect of the dental treatment may correspond to predicted values of final and intermediate positions during the course of the treatment plan. As described herein, a dental treatment protocol may include an orthodontic treatment protocol, a restorative treatment protocol, some combination thereof, and the like.

At block 140, a second 2D image is generated showing the predicted and/or expected effect of the orthodontic treatment. As discussed herein, the image may be a 2D facial image of the patient with the teeth aligned according to the predicted and/or expected effects of the dental treatment plan. In some embodiments, the image may include an estimated texture and/or a projected texture of the patient's teeth, for example, as discussed below with reference to fig. 9 and elsewhere herein. As used herein, a "projected texture" or "predicted texture" of a patient's tooth may include a projection/prediction of tooth texture, and may include the tactile sensation, appearance, consistency, and/or other attributes of the tooth surface. At block 150, the second 2D image generated at block 140 is provided to the user. For example, the image may be rendered for viewing by a patient or dental professional. In some embodiments, the second 2D image may be loaded into memory or retrieved from memory.

Turning to fig. 3B, fig. 3B illustrates a method of determining generic parameters from historical and/or ideal cases in accordance with one or more embodiments herein. At block 360, historical cases and/or ideal cases are obtained. Historical cases and/or ideal cases may be retrieved from a data repository. The historical cases and/or ideal cases may include cases representing previously scanned and segmented arch models. In some embodiments, the historical cases and/or the ideal cases may represent arch models of treated patients (e.g., patients who have received treatment in the past) and/or ideal arch models representing expected effects of various forms of orthodontic treatment. In various embodiments, the historical cases and/or the ideal cases may represent arch models having ideal arch morphologies. In some embodiments, the historical case and/or the ideal case may include areas where the model arch has teeth that correspond to the locations of implants to be implanted in the arch of the patient.

At block 370, the historical cases and/or ideal cases are aligned. In some embodiments, each arch of the historical case and/or the ideal case is aligned in multiple locations. As an example, each arch of historical and/or ideal cases may be aligned in the following three positions: between the central incisors and at each distal end of the left and right sides of each arch. For example, fig. 4 shows a set of dental arches 400 aligned at locations between central incisors 410, at left distal ends 430 of the arch, and at right distal ends 420 of the arch. It should be noted that historical cases and/or ideal cases may be aligned in a wide variety of locations and in a different number of locations without departing from the scope and spirit of the inventive concepts described herein.

Returning to fig. 3B, determining the average arch model and determining the distribution of the arch models may include performing the sub-operations at block 370. For example, averaging over each arch to determineStatorThen determining local deformation of each tooth, andmaking a comparison to determine T_τThen after aligning each tooth, β can be determined_τ。

At block 380, a prediction of the distribution of case-specific parameters is determined. For example, the relative shape, position and rotation of each tooth is determined to construct a distribution of each case specific parameters. For example, the coefficients of the principal components of the surface model of each respective tooth in all retrieved models are determinedDistribution of (2).

The position and orientation of each tooth may be averaged to determine an average position of each tooth and the orientation of each tooth may be averaged to determine an average orientation of each tooth. The average position and average orientation are used to determine

At block 390, the average of the estimated distribution of case-specific parameters is used as a generic parameter for the average tooth position and the average tooth shape.

Fig. 5 illustrates a method 500 of generating a parameterized model of one or more teeth of a patient and converting the parameterized model of one or more teeth to a 3D model of a dental arch, according to some embodiments. The method 500 may be used to generate a 3D model of a dental arch of a patient based on a parameterized model of the patient's teeth.

At block 510, the average shape of each tooth is determined. As an example, the average shape of each tooth is determinedIn some embodiments, as discussed herein, the average shape may be based on a sum from historical cases andand/or the average shape of a set of arches in an ideal case. In some embodiments, for example, the average shape may be rendered on a screen for viewing by a patient or dental professional. For example, the average tooth shape 512 is rendered for viewing. In some embodiments, the average shape may be loaded into or retrieved from memory. The average shape may also be initialized as a set of matrices, one for each tooth.

At block 520, a principal component analysis shape adjustment is performed on the average shape of the tooth. As discussed herein, such adjustments adjust the shape of the teeth based on the particular teeth of the patient, e.g., based on a scan, 2D image, or other imaging technique of the patient's teeth. As an example, average shape of teethAnd adjusting the shape of the principal component analysis. Case-specific coefficients of principal components for each tooth in the modelApplied to the main componentAfter the shape adjustment is completed, in some embodiments, the adjusted shape may be rendered on a screen for viewing by the patient or dental professional, for example. For example, the adjusted tooth shape 522 is rendered for viewing. In some embodiments, the adjusted shape may be stored in memory. The adjusted shape may also be stored as a set of matrices, one for each tooth.

At block 530, an average tooth pose is determined. In some embodiments, as discussed herein, the average tooth pose may be based on an average tooth pose of a set of dental arches from historical and/or ideal cases. In some embodiments, at block 530, each adjusted tooth 522 is placed in its respective average position and orientation determined by the average dental arch. In some embodiments, for example, the average tooth pose may be rendered on a screen for viewing by a patient or dental professional. For example, the average tooth pose 532 is rendered for viewing. In some embodiments, the average dental pose may be loaded into or retrieved from memory. The average tooth pose may also be initialized as a set of matrices, one for each tooth in the tooth pose. In some embodiments, prior to adjusting the shape of the tooth at block 520, the average tooth shape from block 510 may be placed in its respective average tooth pose at block 530. In other words, the order of block 520 and block 530 may be swapped.

At block 540, a tooth pose adjustment is performed on the average tooth pose. As discussed herein, such adjustments adjust the shape of the teeth based on the particular teeth of the patient, e.g., based on a scan, 2D image, or other imaging technique of the patient's teeth. In some embodiments, the pose adjustment T is as discussed above_τBased on the particular dental pose of the patient's dental arch. In some embodiments, at block 540, the position and orientation of each tooth 522 is adjusted such that the tooth is placed in a position and orientation determined or otherwise determined by the position and orientation of the teeth in the imaged patient's arch, as discussed herein. In some embodiments, for example, the adjusted tooth pose may be rendered on a screen for viewing by the patient or dental professional. For example, the adjusted tooth pose 542 is rendered for viewing. In some embodiments, the adjusted tooth pose may be stored in memory. The adjusted tooth poses can also be stored as a set of matrices and/or data structures, e.g., one for each tooth in the tooth pose. In some embodiments, prior to adjusting the shape of the tooth at block 520, the average tooth shape from block 510 may be placed in its respective adjusted tooth pose at block 540. In other words, the order of block 520 and blocks 530 and 540 may be switched such that block 520 occurs after blocks 530 and 540.

At block 550, the arch is scaled such that the generated arch is based on the patient's teeth and arch dimensions. In some embodiments, as discussed above, the arch scaling factor Φ is based on the particular tooth and arch of a particular patient. In various embodiments, for example, when scaling the 3D model for integration into the 2D image, the arch scaling factor may also be based on one or more of the image sizes of the 2D image of the patient. In some embodiments, at block 550, the size of each tooth 522 and arch is adjusted such that the scaled arch matches the size of the patient's arch, as discussed herein, which is determined or otherwise determined, for example, by the size of the teeth and arch in the imaged patient's arch. In some embodiments, for example, the scaled dental arch may be rendered on a screen for viewing by a patient or dental professional. For example, scaled dental arch 552 is rendered for viewing. In some embodiments, the scaled dental arch may be stored in memory. The scaled arch may also be stored as a set of matrices and/or data structures, one for each tooth in the dental pose. In some embodiments, prior to adjusting the shape of the tooth at block 520, the average tooth shape from block 510 may be placed in its respective scaled position and size at block 550. In other words, the order of block 520 and blocks 530, 540, and 550 may be swapped such that block 520 occurs after blocks 530, 540, and 550.

The blocks of method 500 may be performed in an order other than the order shown in fig. 5. For example, blocks 510 and 530 may be performed before blocks 520, 540, and 550. In some embodiments, blocks 510, 520, 530, and 550 may be performed before block 540. These and other modifications may be made to the order of the blocks in the method 500 without departing from the spirit of the present disclosure.

Directing attention to fig. 6, fig. 6 shows a method 600 for constructing a 3D model from 2D images, according to one or more embodiments disclosed herein.

At block 610, a 2D image of a patient is captured. In some embodiments, the 2D image includes one or more images of the mouth and face, head, neck, shoulders, torso, or the entirety of the patient. The 2D image of the patient may include an image of the patient with the patient's mouth in one or more positions. For example, the patient's mouth may be a smile position (e.g., a social smile position), a rest position where muscles are relaxed and lips are ajar, or a retracted anterior open or closed position.

In some embodiments, an image of the patient is taken with an image capture system. The image may be captured using a lens of a predetermined focal length at a distance from the patient. Images may also be captured remotely and then received for processing. Images may be gathered from storage systems, network locations, social media websites, and the like. The image may be a series of images of a video captured from one or more angles. For example, the images may include one or more of a frontal face image and a side image, the side image including one or more three-quarters side images and a full side image.

At block 620, the edges of the patient's oral cavity features are determined. For example, the edges of one or more of the patient's teeth, lips, and gums may be determined. Preliminary determinations of the patient's lips (e.g., the inner edges of the lips defining the bite), teeth, and gum contours can be identified through a machine learning algorithm, such as a convolutional neural network. The machine learning algorithm may be trained based on the pre-identified labels of the lips, teeth, and gums visible within the 2D image of the patient. The initial contour may be a weighted contour such that the machine learning algorithm is confident that a given location in the image (e.g., at each pixel) is the edge or contour of the patient's lips, teeth, or gums.

An initial contour may be extracted from the image. The initial contours may have intensities or other scales applied to them. For example, in a grayscale image of a contour, each pixel may be assigned a value between 0 and 255, which may indicate the confidence that the pixel is a contour, or may indicate the size of the contour at that location.

The pixels representing the contour may then be binarized to change the pixels from a scale of, for example, 0 to 255 to a binarized scale of, for example, 0 or 1, thereby creating a binarized tooth contour. In the binarization process, the value of each pixel is compared with a threshold value. If the value of the pixel is greater than the threshold, it may be assigned a new first value, e.g., 1; and a new second value may be assigned to a pixel, for example if it is less than the threshold, for example, this value is 0.

The binary tooth profile may be thinned, thereby reducing the thickness of the profile to, for example, a single pixel width, resulting in a thinned profile. The width of the contour for thinning may be measured as the shortest distance from a contour pixel adjacent to a non-contour pixel on a first side of the contour to a contour pixel adjacent to a non-contour pixel on a second side of the contour. A single pixel representing the thinned contour at a particular location may be located at a midpoint of the width between the pixel of the first side and the pixel of the second side. After thinning the binarized tooth profile, the thinned profile may be a single width profile at a position corresponding to the midpoint of the binarized profile.

At block 630, the parameterized 3D tooth and arch model is matched to each tooth of the patient shown in the 2D image of the patient. The matching may be based on the edges, also referred to as contours, determined at block 630. Fig. 7A shows an example of a process of matching teeth and arch models to edges. Returning to fig. 6, after identifying and modeling the teeth and arch, or as part of such a process, missing or broken teeth may be inserted into the parameterized 3D tooth and arch model. For example, when a tooth is missing or broken severely, the parameterized model of that tooth can be replaced with an average tooth model to simulate a denture, such as a veneer, crown, or implant. In some embodiments, the missing tooth may remain in the arch as a space. In some embodiments, the broken tooth may remain intact without replacing it with an average shaped tooth.

In the matching process, case-specific parameters of the parameterized arch model are changed and iterated until a match between the parameterized arch model and the teeth shown in the 2D image. Such matching may be determined based on the projection of the edges of the silhouette of the parametric model matching the edges of the lips, teeth and gums identified in the 2D image.

At block 640, a parameterized model of the patient's teeth is rendered. An example of a process of rendering a parameterized model of teeth is described with reference to FIG. 8 and elsewhere herein. In the rendering process, a 3D model of the patient's teeth is formed based on data describing the patient's teeth and dental arch. For example, based on the parameterized 3D model formed at block 630 or described elsewhere herein. In some embodiments, the 3D model is inserted directly into the 2D image of the patient. In these embodiments, the acts in block 640 may be omitted or combined with the acts of block 650, such that the 3D dental model is rendered, for example, in 2D form for insertion into the 2D image.

Optionally, at block 640, prior to rendering into 2D form, simulated treatments may be applied to the 3D model or custom options may be viewed, such as gum line adjustments, jaw position adjustments, missing tooth implants, or broken tooth restorations. In some embodiments, the edges of the parameterized model (e.g., lips, gum line, missing or broken teeth) may be altered to display the simulation results of cosmetic or other treatments and procedures, or to adjust the 2D patient image for custom viewing. For example, a user, such as a dental professional or patient, may adjust the gum line to simulate gum treatment. As another example, a user may choose to show, hide, or repair missing or broken teeth to simulate a dental restoration or replacement procedure. In another example, the user may choose to adjust the jaw position in the simulated image before or after treatment to simulate the appearance of an open jaw, closed jaw, or partially open jaw. The jaw position parameter may be defined by the distance between the tooth surfaces in the upper jaw and the lower jaw, e.g. the distance between the incisor surfaces of the central incisor of the upper jaw and the incisor surfaces of the central incisor of the lower jaw. The jaw position parameters can be defined and varied by the user. For example, the distance between the incisor face of the maxillary central incisor and the incisor face of the mandibular central incisor may vary between-5 mm (which means that the lower incisor overlaps the upper incisor by 5mm) and 10mm (which means that the gap between the upper and lower incisors is 10 mm). The gum line may be adjusted by replacing the patient's gum line mask shape with an average gum line mask (mask) shape from the historical shape data repository. The display of the gum line adjustment may be optionally selected or adjusted by the user. Missing or broken teeth may be replaced with the average tooth shape from the historical shape data store. The display of missing tooth replacement or broken tooth restoration may be optionally selected by the user. For example, simulated treatments or viewing custom options may be rendered on the screen for viewing by a user, patient, or dental professional.

At block 650, the 3D dental model is inserted into the 2D image of the patient. The inner lip edge determined at block 620 may be used to define a contour of the patient's mouth in the 2D image. At block 650, the region of the 2D image defined by the openness may be removed and the 3D model may be placed behind the 2D image and within the openness.

At block 660, texture is applied to the teeth. An example of applying textures is discussed in more detail with reference to FIG. 9. In some embodiments, when applying texture to the teeth, a 2D image of the patient is projected onto a 3D model of the teeth, such as, for example, the parameterized 3D model of the patient's teeth determined at block 630. When projecting a 2D image into a 3D model, the pixels of each location in the 2D image are assigned to a location on the 3D model. In some embodiments, pixel values or texture information from the 2D image is processed before being applied to the 3D model. Other techniques such as image inpainting, blurring, image processing filtering, pix2pix transform techniques, etc. may also be used to generate textures to apply to the surface of the 3D model. The projected pixels at each location on the surface of the 3D model form the texture of the model. For example, pixels projected onto a particular tooth of a patient form the texture of the tooth model. This texture can be applied to the 3D dental model.

Fig. 7A illustrates a method 700 of constructing a patient-specific parameterized model of a patient's teeth, in accordance with some embodiments.

At block 710, the coarse alignment of each corresponding average parameterized tooth is aligned with a respective center of a tooth in the 2D image. The center of the parameterized tooth may be determined based on the center of the area of the projection of the silhouette of the parameterized tooth. The center of the tooth identified in the 2D image of the patient may be determined based on the region center of the region defined by the tooth margin and the corresponding lip margin and/or gum margin. The respective centers of the 2D image tooth and the parameterized tooth may be aligned prior to the desired step and the maximized step of blocks 720 and 730, respectively.

The method 700 may dynamically generate a parameterized tooth model where lip margins and gum margins are matched to teeth in the 2D image at block 710. Additionally or alternatively, lip and gum rims may be applied and/or dynamically adjusted at any of blocks 720, 730, and 740. The 3D tooth model can be computed on-the-fly for varying lip and gum line placement. The parameterized model provided using these models is much more accurate than the parameterized model provided using the gum line alone.

When applying the lip and gum information at block 710, in some embodiments, only the portion of the parameterized model beyond the lip margin and/or gum margin is used to determine the regional center of the teeth. Turning to fig. 7B, an example of a tooth model with a gingival margin 760 and a tooth model with a lip margin 770 is depicted. At any of blocks 710, 720, 730, 740, the position of the gingival margin and/or lip margin may be modified to adjust the fit of the silhouette of the tooth to the visible portion of the corresponding tooth in the 2D image.

Adding the lip margin or gum margin of the teeth can significantly improve the efficiency and the procedure. The result is a process that more realistically matches the tooth model to the 2D image than a process that does not apply the lip margin and gum margin on the tooth model, and that uses a wider variety of photographs.

Returning to FIG. 7A, at block 720, the desired step is performed. In some embodiments, block 720 is performed by a management (EM) engine and/or an engine configured to create a 3D model. At the desired step, a silhouette of the teeth is projected onto the 2D image, and the edges of the silhouette are evaluated from the edges of the teeth in the 2D image determined based on the lip edges, gum edges, and tooth edges. The evaluation may be to determine the normal of the position at the edge of the silhouette and the nearest position in the 2D image with a close normal. The probability that the two edges are identical is then determined. This process may be repeated for each location at the edge of the silhouette.

At block 730, a maximize step of the EM engine is performed. At the maximization step, a small angle approximation is used to provide a maximized analytical solution. The small angle approximation and analytical solution provide an improved solution compared to other methods such as the gaussian-newton iterative method. The small angle approximation greatly reduces computation time and resolves accurate solutions faster.

Blocks 720 and 730 may be performed iteratively for a single parameter or subset of parameters, performing a desired step, then performing a maximize step, then returning to the desired step, and so on until a convergence threshold for the single parameter or subset of parameters is reached. The process may then proceed to block 740.

At block 740, the optimization parameters or subset of parameters are added to the parameterized model. For example, optimization of the parameterized model may start with Φ and T, and then add other parameters after the iterations of EM blocks 720 and 730. For example, T may be added_τAnd then optimized for further iterations through EM blocks 720 and 730. The number of iterations before adding other parameters may vary. In some embodiments, EM blocks 720 and 730 may iterate 3 times, 5 times, 7 times, 10 times, 15 times, 20 times, or any number of times (e.g., any integer). Finally, can beAdded to the parameterized model, processed by EM blocks 720 and 730 until convergence is reached. In this process, outliers can be determined and filtered out. In some embodiments, after block 740, the process 700 may loop back to block 710 instead of looping back to block 720 and proceeding directly to the desired step, where in block 710 the coarse alignment process is performed based on the updated parameterized model.

At block 750, the parameters of the parameterized model are output to another engine, or even to a data warehouse, for later retrieval. For example, as described with reference to fig. 8 and elsewhere herein, the rendering engine may retrieve parameters of the parameterized model for rendering.

Fig. 8 depicts a method 800 of rendering a patient's teeth in an initial position using a parameterized model of the patient's dental arch according to one or more embodiments herein.

At block 810, an average shape for each tooth is determinedIn some embodiments, as discussed herein, the average shape may be based on an average shape of a set of dental arches taken, for example, from historical cases and/or cases representing ideal dental arch morphologies. In some embodiments, for example, the average shape may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the average shape may be loaded into or retrieved from memory. The average shape may also be initialized as a set of matrices, one for each tooth.

At block 820, the average shape of the tooth is determinedAnd adjusting the shape of the principal component analysis. Case-specific coefficients of principal components for each tooth in the modelApplied to the main componentAfter the shape adjustment is completed, in some embodiments, the adjusted shape may be rendered on a screen for viewing by the patient or dental professional, for example. For example, the adjusted tooth shape is rendered for viewing. In some embodiments, the adjusted shape may be stored in memory. The adjusted shape may also be stored as a set of matrices, one for each tooth.

At block 830, an average tooth pose is determined. In some embodiments, as discussed herein, the average tooth pose may be based on an average tooth pose of a set of scanned dental arches. In some embodiments, at block 830, each adjusted tooth is placed at its respective average position and orientation determined by the average dental arch. In some embodiments, for example, the average tooth pose may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the average dental pose may be loaded into or retrieved from memory. The average tooth pose may also be initialized to a set of matrices and/or other data structures, e.g., one matrix for each tooth in the tooth pose. In some embodiments, prior to adjusting the shape of the tooth at block 820, the average tooth shape from block 810 may be placed at its corresponding average tooth pose at block 830. In other words, the order of blocks 820 and 830 may be interchanged.

At block 840, a tooth pose adjustment is made based on the average tooth pose. In some embodiments, the pose adjustment T is as discussed above_τBased on the particular dental pose of the patient's dental arch. In some embodiments, as discussed herein, at block 840, the position and orientation of each tooth is adjusted such that it is placed at a position and orientation determined or otherwise determined by the position and orientation of the teeth in the patient's arch. In some embodiments, for example, the adjusted tooth pose may be rendered on a screen for viewing by the patient or dental professional. In some embodiments, the adjusted tooth pose may be stored in memory. The adjusted tooth poses can also be stored as a set of matrices and/or other data structures, e.g., one for each tooth in the tooth pose. In some embodiments, prior to adjusting the shape of the tooth at block 820, the average tooth shape from block 810 may be placed in its respective adjusted tooth pose at block 840. In other words, the order of block 820 and blocks 830 and 840 may be switched such that block 820 occurs after blocks 830 and 840.

At block 850, the arch is scaled so that the resulting arch has dental dimensions according to the patient's teeth and arch size. In some embodiments, as discussed above, the arch scaling factor Φ is based on the particular tooth and arch of a particular patient. In some embodiments, at block 850, the size of the arch is adjusted such that the scaled arch matches the size of the patient's arch, as discussed herein, which is determined or otherwise determined, for example, by the size of the teeth and arch in the patient's arch. In some embodiments, for example, the scaled dental arch may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the scaled dental arch may be stored in memory. The scaled arch may also be stored as a set of matrices and/or data structures, e.g., one for each tooth in the dental pose. In some embodiments, prior to adjusting the shape of the tooth at block 820, the average tooth shape from block 810 may be placed at its respective scaled position and size at block 850. In other words, the order of block 820 and blocks 830, 840, and 850 may be switched such that block 820 occurs after blocks 830, 840, and 850.

The blocks of method 800 may be performed in an order other than the order shown in fig. 8. For example, blocks 810 and 830 may be performed before blocks 820, 840 and 850. In some embodiments, block 810, block 820, block 830, and block 850 may be performed before block 840. These and other modifications may be made to the order of the blocks in method 800 without departing from the spirit of the present disclosure.

Fig. 9 depicts a method 900 of building a 3D model and applying textures to the 3D model according to one or more embodiments herein. Texture may help provide details such as color information to the 3D model of the patient's teeth. As described herein, the process 900 uses images of a patient's teeth to provide realistic texture to the patient's teeth.

Thus, as described elsewhere herein, for example, in the discussion related to fig. 5, at block 910, a 3D model of the patient's teeth and a 2D image of the patient are acquired. The 2D image and the 3D model should depict the same position of the tooth, e.g. the 3D model may be a parametric model derived from the 2D image of the patient.

At block 920, a 2D image of the patient's teeth is projected onto the 3D model and the image is aligned with the model. This alignment may be done by matching contours in the 3D model with contours in the 2D image. As described elsewhere herein, the contour may include the determined lip margin, gum margin, and tooth margin.

At block 930, color information from the 2D image is mapped as a texture to the 3D model. Color information may include lighting conditions such as specular highlights, accurate tooth coloring and texture, and other tooth features such as dentition, incisors, and the like. Once the texture is mapped to the 3D model, the teeth in the 3D model may be repositioned, for example, to depict an estimated effect and/or an expected effect of a dental treatment plan (e.g., an orthodontic treatment plan, a restorative treatment plan, some combination thereof, etc.). Such final positions include accurate color and tooth characteristic information as well as accurate 3D positioning of the patient's teeth, and may be used, for example, to simulate the predicted and/or expected effects of a dental treatment regimen (e.g., final orthodontic position) of the patient's teeth, as described with reference to fig. 10A, 10B, and/or elsewhere herein.

In some embodiments, the texture model is adjusted to simulate a clinical treatment or beautify a treatment. For example, the color may be adjusted to simulate a tooth whitening procedure. Simulating the treatments may include generating masks from 2D projections of the 3D tooth model, where the model is in an arrangement for insertion into the 2D image, e.g., in an initial or final position. The mask may be applied in a 2D image of the patient's teeth in an initial or final position. Color adjustment or whitening may also be applied to the mask area. The color adjustment and whitening parameters may be optionally selected and adjusted by the user. For example, color adjustments and whitening may be rendered on a screen for viewing by a user, patient, or dental professional.

Fig. 10A illustrates a method 1000 for simulating an estimated and/or expected effect of a dental treatment regimen for a patient's teeth according to one or more embodiments herein.

At block 1010, a model of a set of average dental arches is constructed. In some embodiments, the model is a parameterized 3D model based on a set of scanned historical dental arches and/or an average dental arch of a dental arch representing an ideal dental arch morphology. In some embodiments, the scan is from an initial position of the patient's teeth. In some embodiments, the scan is from the patient after orthodontic treatment is completed. In other embodiments, the scan is performed without regard to whether the patient has received orthodontic treatment. In some embodiments, the historical cases and/or the ideal cases may represent arch models of treated patients (e.g., patients who have received treatment in the past) and/or ideal arch models representing expected effects of various forms of orthodontic treatment. Block 1010 may include process 350 described with reference to fig. 3B. At block 1010, a case is acquired. These cases may be retrieved from a data repository and may include previously scanned and segmented arch models.

These arches may also be aligned. Each arch in the data store may be aligned in multiple positions. For example, each arch may be aligned in three positions: between the central incisors and at each distal end of the left and right sides of each arch. For example, fig. 4 shows a set of dental arches 400 aligned at locations between central incisors 410, at the left distal end 430 of the arch, and at the right distal end 420 of the arch.

In some embodiments, determining the average arch model and determining the distribution of the arch models comprises performing the substeps. For example, each historical arch may be rescaled to determine Φ, then averaged for each arch to determineThen determining local deformation of each tooth and comparingMaking a comparison to determine T_τThen after aligning each tooth, β is determined_τ。

An estimate of the distribution of case-specific parameters is also determined. For example, the relative shape, position and rotation of each tooth is determined to construct a distribution of each case specific parameters. For example, the coefficients of the principal components of the surface model of each respective tooth in all retrieved models are determinedDistribution of (2).

Finally, the average of the estimated distribution of case-specific parameters can be used as a general parameter for the average tooth position and the average tooth shape.

At block 1020, a 2D image of the patient is captured. In some embodiments, the 2D image includes one or more images of the mouth and face, head, neck, shoulders, torso, or the entirety of the patient. The 2D image of the patient may include an image of the patient with the patient's mouth in one or more positions. For example, the patient's mouth may be a smile position (e.g., a social smile position), a rest position where muscles are relaxed and lips are ajar, or a retracted anterior open or closed position.

In some embodiments, an image of the patient is acquired, for example, an image of the patient is taken with an image capture system. The image may be captured using a lens of a predetermined focal length at a distance from the patient. In some implementations, the image of the patient is obtained from a computer storage device, a network location, a social media account, or the like. Images may also be captured remotely and then received for processing. The images may be a series of images of a video taken from one or more angles. For example, the images may include one or more of a frontal face image and a side image, the side image including one or more three-quarters side images and a full side image.

At block 1030, a 3D model of the patient's teeth is constructed from the 2D images of the patient's teeth. The 3D model may be based on a parameterized model constructed according to the process described with reference to fig. 6, wherein edges of oral features of the patient are determined. For example, the edges of one or more of the patient's teeth, lips, and gums may be determined. The preliminary determination of the patient's lips (e.g., the inner edges of the lips defining the bite), teeth, and gum contours may be identified by a machine learning algorithm such as a convolutional neural network.

Then, an initial contour may be extracted from the image. Then, the pixels representing the contour may be binarized. The binarized profile of the tooth is thinned, thereby reducing the thickness of the profile to, for example, a single pixel width, resulting in a thinned profile.

Using the thinned or otherwise contoured, the parameterized 3D tooth and arch model is matched to each tooth of the patient depicted in the 2D image of the patient. This matching is based on the contours determined above. After identifying and modeling the teeth and arches, or as part of such a process, missing or broken teeth may be inserted into the parameterized 3D tooth and arch model.

At block 1040, case-specific parameters are estimated. In some embodiments, case-specific parameters may be determined at block 1030 as part of a model build based on, for example, the process described in fig. 6. In the matching process or at block 1040, case-specific parameters of the parameterized arch model are changed and iterated until the parameterized arch model matches the teeth depicted in the 2D image. As described herein, such matching may be determined based on the projection of the edges of the silhouette of the parametric model matching the edges of the lips, teeth, and gums identified in the 2D image.

At block 1050, the patient's teeth are rendered according to the simulated effect of the dental treatment plan using case specific parameters of the patient's tooth shape or arch scale, and using only the average of the tooth positions. Fig. 11 shows an example of a method of rendering teeth according to a dental treatment plan and/or according to an expected effect of a dental treatment plan, using appropriate case-specific parameter values and average parameter values.

Optionally, at block 1050, other changes such as gum line adjustments, jaw position adjustments, missing tooth replacement, or broken tooth restoration may be applied to the 3D model before rendering into 2D form. In some embodiments, the edges and other features of the model, such as lips, gum line, and missing or broken teeth, may be altered to display the simulation results of the therapeutic procedure, or to adjust the 2D image of the patient for a custom view. For example, the user may adjust the gum line to simulate gum treatment. In another example, the user may choose to show, hide, or repair missing or broken teeth to simulate a dental restoration or replacement procedure. In this case, the missing tooth or broken tooth may be replaced with an ideal tooth based on the ideal parameters discussed above. The missing tooth or broken tooth may be replaced based on one tooth in the historical data repository or with the patient's own tooth, e.g., corresponding teeth from opposite sides of the patient's dental arch may be used. For example, if the upper left canine tooth is missing or broken, a mirror image model of the patient's upper right canine tooth may be used at the location of the missing upper left canine tooth. In another example, the user may choose to adjust the jaw position in the simulated image before or after treatment to simulate the appearance of an open jaw, closed jaw, or partially open jaw. The jaw position parameter may be defined by the distance between the tooth surfaces in the upper jaw and the lower jaw, e.g. the distance between the incisor surfaces of the central incisor of the upper jaw and the incisor surfaces of the central incisor of the lower jaw. The jaw position parameters can be defined and varied by the user. The gum line may be adjusted by replacing the patient's gum line mask shape with an average gum line mask shape from the historical average shape database. The display of the gum line adjustment may be optionally selected or adjusted by the user. Missing or fractured teeth may be replaced with an average tooth shape from a historical average shape database. The display of missing tooth replacement or broken tooth restoration may be optionally selected by the user. For example, simulated treatments may be rendered on the screen or custom options viewed for the user, patient, or dental professional.

At block 1060, the 3D model is inserted into the 2D image of the patient. For example, as part of the model construction process, a 3D rendering of the teeth may be placed in the patient's mouth as defined by the lip contour determined at block 1030. In some embodiments, in block 1060, the texture model is adjusted to simulate a clinical beautification treatment. For example, the color may be adjusted to simulate a tooth whitening procedure. The mask may be generated from a 2D projection of the 3D dental model. The mask may be applied to a 2D image of the patient's teeth at an initial or final position. Color adjustment or whitening may also be applied to the mask areas. The color adjustment and whitening parameters may be optionally selected and adjusted by the user.

The simulated treatment performed at block 1050 or block 1060 or viewing custom options may be selected, deselected, or adjusted by the user on the projected 2D image. The parameters may be adjusted to simulate treatments such as whitening, gum line treatment, tooth replacement or tooth restoration, or to view custom options such as displaying open, closed, or partially open jaws. In one example, the user may optionally adjust the color parameters to simulate tooth whitening. The user may adjust the color parameters to increase or decrease the degree of tooth whitening. In another example, the user may optionally adjust the gum line parameters for oral hygiene to simulate gum line treatment or changes. The user can adjust the gum line parameters to increase the gum line (reduce exposure of the tooth surface) to simulate gum recovery due to, for example, improved oral hygiene or gum line treatment, or to decrease the gum line (increase exposure of the tooth surface) to simulate gum recession due to, for example, poor oral hygiene. In another example, the user may optionally select tooth replacement parameters. The user may select tooth replacement parameters to replace one or more missing, broken, or destroyed teeth with a desired tooth (e.g., a tooth from a historical data store or the patient's own tooth). In another example, the user can optionally adjust the jaw line parameters for custom viewing of the jaw position. The user may increase the distance between the teeth in the upper jaw and the teeth in the lower jaw to simulate opening the jaw, or decrease the distance between the teeth in the upper jaw and the teeth in the lower jaw to simulate closing the jaw. In this case, the user can simulate the appearance of orthodontic, beautification, or clinical treatments at various jaw positions.

Fig. 10B depicts a process 1005 for simulating a final treatment position of a patient's teeth using matching dental arches identified from the parameterized search of the treatment plan data store.

As depicted at block 1020 of fig. 10A, at block 1015, a 2D image of the patient's teeth is captured, and as at block 1030 of fig. 10A, a 3D model of the patient's teeth is constructed from the 2D image of the patient's teeth at block 1025. As at block 1040 of fig. 10A, case-specific parameters are estimated at block 1035.

At block 1045, the tooth shape parameters of the patient's teeth from the 3D model are used to perform a parameterized search of the treatment plan data store. In matching the shape of the patient's teeth to the shape of the historically treated teeth in the data store, the shape of the teeth in the parameterized 3D model of the patient is compared to the parameterized shape of the teeth in each historical record in the treatment data store. When there is a match between tooth shapes, the final tooth position and orientation or the final arch model of the matching record can be used in the simulated treatment plan. A parameterized template arch may be identified based on a comparison of tooth shape parameters, thereby identifying a matching template arch. In some embodiments, for example, the matching may be based on the closest matching template arch. In some embodiments, the template shape may be loaded into or retrieved from memory. The template shape may also be initialized as a set of matrices, one for each tooth. Once a match is found in the records within the treatment data repository, the final arch model from the matching records is retrieved and can be used as the basis for the target final tooth position for the patient.

In some embodiments, the tooth positions and orientations in the matching records are used as a basis for the tooth positions and orientations of the target final tooth positions for the patient. In using the tooth positions and orientations, the patient's parameterized arch model may be modified with the tooth positions and orientations in the matching records, or the matching records may be updated with the shapes of the patient's teeth. In some embodiments, a model of each tooth of the patient with unaltered tooth shape is placed under the final tooth pose determined from the matching records. In some embodiments, a model of each tooth of the patient whose shape is adjusted (e.g., the tooth shape from the matching record) is placed in the final tooth pose determined from the matching record. Optionally, a tooth pose adjustment can be made to adjust the position of the teeth in the final tooth pose.

In some embodiments, the final arch model from the matching records is used as the final simulated position for patient treatment in a 2D rendering. In some embodiments, the positions at which the teeth from the final arch model of the matching record are placed are based on the positions of the teeth in the 2D picture of the patient. In addition, the positions of the teeth on which the final arch model is placed are based on the positions of the teeth in the parameterized 3D model of the patient. Alternatively, the final arch model whose position is adjusted from the 2D picture or 3D model of the patient may be used as the simulated position in the 2D rendering.

The shape of the template teeth may be adjusted based on the parameterized 3D model of the patient to more closely match the patient's tooth shape. Alternatively, a principal component analysis shape adjustment may be made to the die tooth shape. After the shape adjustment is completed, in some embodiments, the adjusted shape may be rendered on a screen for viewing by a user, patient, or dental professional, for example. In some embodiments, the adjusted shape may be stored in memory. The adjusted shape may also be stored as a set of matrices, one for each tooth. In some embodiments, the teeth in the matching template from the treatment data store are replaced with the teeth of the patient.

The template arch may be scaled so that the resulting arch is based on the patient's teeth and arch dimensions. In some embodiments, as discussed above, the template arch scaling factor Φ is based on the particular tooth and arch of a particular patient. In some embodiments, the template arch scaling factor may also be based on one or more of the image dimensions of the 2D image of the patient, for example, when scaling the 3D model for integration into the 2D image. In some embodiments, as discussed herein, the dimensions of each template tooth and template arch are adjusted such that the scaled template arch matches the dimensions of the patient's arch, which are determined, for example, by the dimensions of the scanned teeth and arch in the patient's arch or otherwise. In some embodiments, for example, the scaled template dental arch may be rendered on a screen for viewing by a user, patient, or dental professional. In some embodiments, the scaled template dental arch may be stored in memory. The scaled template arch may also be stored as a set of matrices, one for each tooth in the tooth pose. In some embodiments, the desired template tooth shape may be placed in its corresponding scaled position and size prior to adjusting the shape of the tooth.

At block 1045, a simulation treatment or viewing customization option, such as gum line adjustment, jaw position, missing tooth implantation, or broken tooth restoration, may be applied to the 3D model before rendering into 2D form. Simulated treatments may be applied and adjusted or custom option changes may be viewed as described in block 640.

At block 1055, a 3D model is inserted into the 2D image of the patient. For example, a 3D rendering of the teeth may be placed in the patient's mouth defined by the lip contours, which are determined at block 1025 as part of the model building process. Rendering may be performed with or without implementing simulated treatment or viewing custom options in block 1045. In some embodiments, at block 1055, the texture model is adjusted to simulate clinical treatment. For example, the color may be adjusted to simulate a tooth whitening procedure. The mask may be generated from a 2D projection of the 3D dental model. The mask may be applied to a 2D image of the patient's teeth in either the initial or final position. Color adjustment or whitening may also be applied to the mask areas. The color adjustment and whitening parameters may be optionally selected and adjusted by the user.

The simulated treatment performed at block 1045 or block 1055 and viewing custom options may be selected, deselected, or adjusted by the user on the projected 2D image. The parameters may be adjusted to simulate treatments such as whitening, gum line treatment, tooth replacement or tooth restoration, or to view custom options such as displaying open, closed, or partially open jaws.

Fig. 11 shows an example of a method of rendering teeth according to an estimated and/or expected effect of a dental treatment plan based on a parameterized model of a patient's dental arch using patient-derived values of case-specific parameters of scale and tooth shape, and using only averages of tooth positions (e.g., averages derived from historical and/or ideal dental arches).

At block 1110, an average shape for each tooth is determinedIn some embodiments, the average shape may be based on the average shape of the dental arches of a set of scans, as discussed herein. In some embodiments, for example, the average shape may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the average shape may be loaded into or retrieved from memory. The average shape may also be initialized as a set of matrices, one for each tooth.

At block 1120, the average shape of the tooth is determinedAnd adjusting the shape of the principal component analysis. Case-specific coefficients of principal components for each tooth in the modelApplied to the main componentAfter the shape adjustment is completed, in some embodiments, for example, it may be on-screenRendering the adjusted shape for viewing by the patient or dental professional. In some embodiments, the adjusted shape may be stored in memory. The adjusted shape may also be stored as a set of matrices and/or data structures, e.g., one for each tooth.

At block 1130, an average tooth pose is determined. In some embodiments, as discussed herein, the average tooth pose may be based on an average tooth pose of a set of scanned dental arches. In some embodiments, at block 1130, each adjusted tooth is placed at its respective average position and orientation as determined by the average dental arch. In some embodiments, for example, the average tooth pose may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the average dental pose may be loaded into or retrieved from memory. The average tooth pose may also be initialized as a set of matrices, one for each tooth in the tooth pose. In some embodiments, prior to adjusting the shape of the tooth at block 1120, the average tooth shape from block 1110 may be placed in its respective average tooth pose at block 1130. In other words, the order of block 1120 and block 1130 may be swapped.

At block 1140, the arch is scaled so that the generated arch has dental dimensions according to the patient's teeth and arch size. In some embodiments, as discussed above, the arch scaling factor Φ is based on the particular tooth and arch of a particular patient. In some embodiments, as discussed herein, at block 1140, the arch is adjusted such that the scaled arch matches the size of the patient's arch, which is determined or otherwise determined, for example, by the size of the scanned teeth and arch in the patient's arch. In some embodiments, for example, the scaled dental arch may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the scaled dental arch may be stored in memory. The scaled arch may also be stored as a set of matrices, one for each tooth in the dental pose. In some embodiments, prior to adjusting the shape of the tooth at block 1120, the average tooth shape from block 1110 may be placed in its respective scaled position and size at block 1140. In other words, the order of block 1120 and blocks 1130 and 1140 may be swapped such that block 1120 occurs after blocks 1130 and 1140.

Prior to rendering at block 1150, simulated treatments such as gum line adjustments, jaw positions, missing tooth insertions, or broken tooth restorations, or viewing customization options can be applied to the 3D model. As described in block 640, simulated treatments may be applied and adjusted or custom options viewed. The simulated treatment may be applied before or after arch scaling (e.g., before or after block 1140) or to view custom options.

At block 1150, the 3D model of the patient's teeth determined at block 1140 is rendered for viewing or the final 3D model is stored for later on-screen rendering. The 3D model may be rendered for viewing or stored for later on-screen rendering, regardless of whether simulated treatment is performed or custom options are viewed.

At block 1160, a texture is applied to the 3D model of the patient's teeth at the estimated final position and/or the expected final position. For example, a texture determined based on a 2D image of a patient's teeth, e.g., according to process 900 in FIG. 9, may be applied to the 3D model. In some embodiments, the texture application process of block 1160 may occur during the rendering process of block 1150 or as part of the rendering process of block 1150. During texture application, either at block 1160 or during the rendering process of block 1150, the texture model may be adjusted to simulate treatment or for customizable viewing. Simulated treatment or viewing customization options, including color adjustments to simulate tooth whitening, for example, may be applied to the 3D model, or may occur as part of the rendering process. Additionally, color adjustments may be made as described at block 1060. Simulating treatment and viewing custom options, color adjustments may be adjusted by the user.

Optionally, step 1110 may be replaced with a parameterized search of the treatment plan data store to identify matching template models from the patient's tooth shape. Steps 1120-1160 may then be performed as described using the template arch and tooth shape instead of the average arch or tooth shape to determine the ideal tooth pose, rather than the average tooth pose.

Fig. 12 illustrates a system 1200 for simulating an estimated and/or expected effect of orthodontic treatment according to some embodiments. In the example of fig. 12, system 1200 includes computer-readable medium 1210, dental scanning system 1220, dental treatment planning system 1230, dental treatment simulation system 1240, and image capture system 1250. One or more elements of system 1200 may include elements such as those described with reference to the computer system shown in fig. 20. One or more elements of system 1200 may also include one or more computer-readable media comprising instructions that, when executed by a processor (e.g., a processor of any of systems 1220, 1230, 1240, and 1250), cause the respective one or more systems to perform processes described herein.

The dental scanning system 1220 may include a computer system configured to capture one or more scans of a patient's dentition. The dental scanning system 1220 may include a scan engine for capturing 2D images of a patient. These images may include images of the patient's teeth, face, and jaw. These images may also include x-ray or other subsurface images of the patient. The scan engine may also capture 3D data representing the patient's teeth, face, gums, or other aspects of the patient.

The dental scanning system 1220 may also include a 2D imaging system, such as a still or video camera, an x-ray machine, or other 2D imager. In some embodiments, the dental scanning system 1220 further includes a 3D imager such as an intraoral scanner or an impression scanner. As described herein with reference to fig. 1-11, the dental scanning system 1220 and associated engines and imagers may be used to capture historical scan data for determining historical average parameters of a 3D parameterized dental model. For example, as described herein with reference to fig. 1-11, the dental scanning system 1220 and associated engines and imagers may be used to capture 2D images of the patient's face and dentition for constructing a 3D parametric model of the patient's teeth.

The dental treatment simulation system 1240 may include a computer system configured to simulate one or more predicted effects and/or expected effects of a dental treatment plan. In some embodiments, the dental treatment simulation system 1240 obtains photographs and/or other 2D images of the customer/patient. The dental treatment simulation system 1240 may also be configured to determine teeth, lips, gums, and/or other edges associated with teeth in the 2D image. As described herein, the dental treatment simulation system 1240 may be configured to match tooth and/or arch parameters to teeth, lips, gums, and/or other edges. The dental treatment simulation system 1240 may also render a 3D tooth model of the patient's teeth. The dental treatment simulation system 1240 may gather information related to historical dental arches and/or ideal dental arches that represent predicted treatment effects. In various embodiments, the dental treatment simulation system 1240 may insert and align the 3D tooth model into the 2D image of the patient and with the 2D image of the patient, etc., to render a 2D simulation of the estimated effect of the orthodontic treatment. The dental treatment simulation system 1240 may include a photo parameterization engine that may further include an edge analysis engine, an EM analysis engine, a tooth coarse alignment engine, and a 3D parameterization conversion engine. The dental treatment simulation system 1240 may also include a parameterized treatment prediction engine that may also include a treatment parameterization engine, a scanned tooth normalization engine, and a treatment plan re-modeling engine. The dental treatment simulation system 1240 and its associated engines may perform the processes described above with reference to fig. 5-9.

The dental treatment planning system 1230 may include a computer system configured to implement a treatment plan. The dental treatment planning system 1230 may include a rendering engine and interface for visualizing or otherwise displaying the simulated effects of the dental treatment plan. For example, the rendering engine may render a visualization of the 3D model described herein, e.g., at block 140 of fig. 1, at blocks 640, 650, 660 of fig. 6, process 800 described with reference to fig. 8, at blocks 910, 920, 930 of fig. 9, block 1150 of fig. 10, and block 1150 of fig. 11. The dental treatment planning system 1230 can also determine an orthodontic treatment plan for moving the patient's teeth from the initial position to the final position based, for example, in part on the 2D image of the patient's teeth. The dental treatment planning system 1230 may be operable to provide image viewing and manipulation such that the rendered images may be scrollable, rotatable, scalable, and interactive. The dental treatment planning system 1230 may include graphics rendering hardware, one or more displays, and one or more input devices. Some or all of the dental treatment planning system 1230 may be implemented on a personal computing device, such as a desktop computing device, or a handheld device, such as a mobile phone. In some embodiments, at least a portion of the dental treatment planning system 1230 may be implemented on a scanning system, such as the dental scanning system 1220. Image capture system 1250 may include a device configured to acquire images, including images of a patient. The image capture system may include any type of mobile device (iOS device, iPhone, iPad, iPod, etc., Android device, portable device, tablet), PC, camera (DSLR camera, film camera, video camera, still camera, etc.). In some implementations, the image capture system 1250 includes a set of stored images, such as images stored on a storage device, a network location, a social media website, or the like.

Fig. 13 illustrates an example of one or more elements of a dental treatment simulation system 1240 according to some embodiments. In the example of fig. 13, the dental treatment simulation system 1240 includes a photo collection engine 1310, a photo data warehouse 1360, a photo parameterization engine 1340, a case management engine 1350, a reference case data warehouse 1370, and a treatment rendering engine 1330.

The photo gathering engine 1310 may implement one or more automated agents configured to retrieve photos of the selected patient from the photo datastore 1360, the image capture system 1250, and/or scans from the dental scanning system 1220. The photo collection engine may then provide the one or more photos to the photo parameterization engine 1340. The photo collection engine may then provide the one or more photos to the photo parameterization engine 1340 and/or other modules of the system.

Photo data store 1360 may include a data store configured to store photos of patients (e.g., photos of their faces). In some embodiments, the photographs are 2D images including one or more images of the mouth and face, head, neck, shoulders, torso, or the entirety of the patient. The 2D image of the patient may include an image of the patient with the patient's mouth in one or more positions. For example, the patient's mouth may be a smile position (such as a social smile position), a rest position where muscles are relaxed and lips are ajar, or a retracted anterior open or closed position. In some embodiments, an image of the patient is taken with an image capture system. The image may be captured using a lens of a predetermined focal length at a distance from the patient. Images may also be captured remotely and then received for processing. The images may be a series of images of a video taken from one or more angles. For example, the images may include one or more of a frontal face image and a side image, the side image including one or more three-quarters side images and a full side image.

The photo parameterization engine 1340 may implement one or more automated agents configured to build a parameterized 3D model based on 2D images of the patient from the photo data store 1360 and parameterized models and mean data from the parameterized therapy prediction engine. The margin analysis engine 1342 may implement one or more automatic agents configured to determine the margins of teeth, lips, and gums within the patient's picture, e.g., as described with reference to fig. 6. In particular, a preliminary determination of the patient's lips (e.g., the inner edges of the lips defining the bite), teeth, and gum contours can be identified by a machine learning algorithm, such as a convolutional neural network.

The coarse alignment engine 1344 may implement one or more automatic agents configured to receive the 2D image and/or the related edges and to perform a coarse alignment between the identified edges and the average tooth model, e.g., as described with reference to fig. 7A, wherein the respective centers of the teeth in the 2D image and the silhouette of the parameterized teeth may be aligned before sending the 2D image and/or the related edges along with the silhouette and its related tooth parameters to the EM engine 1346.

The Expectation Maximization (EM) engine 1346 may implement one or more automatic agents configured to perform an expectation maximization analysis between edges of the 2D image and parameters of the 3D parametric model, for example, as described with reference to fig. 7A, and in particular, blocks 720, 730, and 740.

Once the EM analysis engine 1346 completes the matching of the parameterized 3D model to the 2D image of the patient, the parameterized results are sent to the 3D parameter conversion engine 1348, and the 3D parameter conversion engine 1348 may convert the principal component analysis output by the EM analysis engine into case specific parameters that define the 3D model of the patient's teeth from the parameterized model for use by the parameterized treatment prediction engine 1350, as described above.

The case management engine 1350 can include a case parameterization engine 1352, a scanned teeth normalization engine 1354, and a treatment plan simulation engine 1356. The case management engine 1350 can implement one or more automated agents configured to define parameterized models for representing the patient's teeth and dental arches, determine mean data that parametrically represents the mean positions of the patient's teeth and dental arches based on the treatment plan retrieved from the treatment plan data store 1370, and simulate treatment of the patient-specific teeth based on the parameterized models, the mean tooth positions, and the patient-specific teeth.

Case parameterization engine 1352 may implement one or moreAn automated agent or agents configured to define a parameterized 3D model for use in modeling teeth. For example, the definition of the parameterized 3D model may be as described above with reference to equation (2). The treatment parameterization engine 1352 defines not only equations, but also the schema of the parameters. For example, T may be indicative of tooth position_τDefined as a 4 x 4 matrix including tooth positions and 3 axes of rotation. Similarly, the shape of the teeth may be expressedDefined as a 2500 x 3 matrix, where each vertex of the sphere maps to a location on the surface of the tooth model, each vertex location being an x, y, and z specific location. For lower definition or higher definition models, fewer vertices or more vertices may be used. Further discussion of the parameters defined by the treatment parameterization engine 1352 is described elsewhere herein (e.g., with reference to fig. 2-5).

The scanned tooth normalization engine 1354 may implement one or more automated agents configured to parameterize the scanned teeth of the set of treatment plans collected from the treatment plan data store 1370 and then determine an average set of general generic parameters for the parameterized model. Scanned tooth normalization engine 1354 may perform process 350 described with reference to fig. 3B. For example, the scan teeth normalization engine 1354 may align dental arches within historical cases retrieved from the data warehouse 1370. Each arch in the data store is aligned in three positions: between the central incisors and at each distal end of the left and right sides of each arch. The position and orientation of each tooth is then determined based on its position and orientation relative to the alignment point.

The scanning teeth normalization engine 1354 may implement one or more automated agents configured to determine the distribution of parameters among cases. For example, each historical arch may be rescaled to determine Φ, then averaged for each arch to determineThen determining local deformation of each tooth and comparingMaking a comparison to determine T_τThen after aligning each tooth, β is determined_τ。

From this data, the relative shape, position and rotation of each tooth was determined to construct a distribution of each case specific parameters. For example, the coefficients of the principal components of the surface model of each respective tooth in all retrieved models are determinedDistribution of (2).

In some embodiments, a template data store of parameterized template models may be generated using scanned teeth of a set of treatment plans gathered from the treatment plan data store. The scan teeth normalization engine 1354 may align the dental arches in the historical cases retrieved from the reference case data warehouse 1370. Each arch in the reference case data warehouse is aligned in three positions: between the central incisors and at each distal end of the left and right sides of each arch. The position and orientation of each tooth is then determined based on its position and orientation relative to the alignment point. The parameters of the parameterized template model in the data store are searchable. The data store may be used to match a patient 3D model (e.g., generated from a 2D model or from a 3D scan) to the closest template in the data store based on tooth shape and arch shape to identify a matching template model. The closest matching template model may be used to determine the final tooth position and orientation of the patient's teeth.

The treatment plan simulation engine 1356 may implement one or more automated agents configured to simulate the predicted and/or expected effects of a dental treatment plan for a particular patient's teeth using the parameterized model defined by the treatment parameterization engine 1352, the general parameters from the scanned tooth normalization engine, and the case-specific parameters from the photo parameterization engine 1340, for example, as described with reference to fig. 10 and 11.

Treatment protocol simulation engine 1356 retrieves or otherwise determines the average shapeIn some embodiments, the average shape may be retrieved from the scanned tooth normalization engine 1354. In some embodiments, the average shape may be loaded into or retrieved from memory. The average shape may also be initialized as a set of matrices, one for each tooth.

Treatment plan simulation engine 1356 average shape of teethAnd adjusting the shape of the principal component analysis. Case-specific coefficients determined from the photo parameterization engine 1340 for the principal components are applied to each tooth in the modelApplied to the main componentAfter the shape adjustment is completed, in some embodiments, the adjusted shape may be rendered on a screen for viewing by the patient or dental professional, for example. In some embodiments, the adjusted shape may be stored in memory. The adjusted shape may also be stored as a set of matrices, one for each tooth.

The treatment plan simulation engine 1356 determines the average tooth pose. In some embodiments, as discussed above, the average tooth pose may be based on the average tooth pose of a set of scanned dental arches. In some embodiments, each adjusted tooth is placed in its respective average position and orientation as determined by the average dental arch. In some embodiments, for example, the average tooth pose may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the average dental pose may be loaded into or retrieved from memory. The average tooth pose may also be initialized as a set of matrices, one for each tooth in the tooth pose.

In some embodiments, the treatment plan re-modeling engine 1356 uses the matched tooth position and orientation coordinates from the ideal parametric data store and applies the shape and texture of the 3D model of the patient generated from the 2D images to the tooth position and orientation to generate the dental arch.

The treatment plan simulation engine 1356 scales the arch so that the generated arch is based on the patient's teeth and arch dimensions. In some embodiments, as discussed above, the arch scaling factor Φ is based on the particular tooth and arch of a particular patient. In some embodiments, as discussed herein, the arch is adjusted such that the scaled arch matches the size of the patient's arch, which is determined or otherwise determined, for example, by the size of the scanned teeth and arch in the patient's arch. In some embodiments, for example, the scaled dental arch may be rendered on a screen for viewing by a patient or dental professional. In some embodiments, the scaled dental arch may be stored in memory. The scaled arch may also be stored as a set of matrices, one for each tooth in the dental pose. The scaled dental arch may be sent to the treatment rendering engine 1330.

The treatment rendering engine 1330 renders the patient's teeth and 2D images in the final position, for example, as determined by the parametric treatment prediction engine 1350, and in particular, the treatment plan simulation engine 1356 located in the parametric treatment prediction engine 1350. The therapy rendering engine 1330 may perform some of the processes described with reference to fig. 9, 10, and 11. In particular, the rendering process described with reference to fig. 9, 10, 11 and elsewhere herein.

Fig. 14 illustrates an exemplary tooth repositioning appliance or appliance 1500 that may be worn by a patient to effect a gradual repositioning of individual teeth 1502 in the jaw. The appliance may include a shell (e.g., a continuous polymeric shell or a segmented shell) having tooth receiving cavities that receive and resiliently reposition the teeth. The appliance, or one or more portions thereof, may be manufactured indirectly using a solid model of the teeth. For example, an appliance (e.g., a polymeric appliance) may be formed using a solid model of a tooth and an appropriate number of layers of sheets of polymeric material. Solid models of teeth (e.g., solid molds) can be formed by various techniques including 3D printing. The orthosis may be formed by thermoforming the orthosis over a solid former. In some embodiments, the solid appliance is directly manufactured by a digital model of the appliance, for example using additive manufacturing techniques. In some embodiments, the solid appliance may be manufactured by various direct forming techniques, such as 3D printing. The appliance may be fitted over all or less than all of the teeth present in the upper or lower jaw. The appliance may be specifically designed to adapt to the patient's teeth (e.g., the topography of the tooth receiving cavity (topographies) matches the topography of the patient's teeth) and may be manufactured based on positive or negative models (positive or negative models) of the patient's teeth generated by stamping, scanning, and the like. Alternatively, the appliance may be a universal appliance configured to receive the teeth but not necessarily shaped to match the topography of the patient's teeth. In some cases, only some of the teeth received by the appliance are repositioned by the appliance, while other teeth may provide a base or anchor region that holds the appliance in place when the appliance applies a force to one or more teeth that are targeted for repositioning. In some cases, some or most, and even all, of the teeth are repositioned at some point during treatment. The moved teeth may also serve as a base or anchor to hold the appliance in place when the appliance is worn by the patient. In some embodiments, no further securing of the orthosis is providedWires or other means of attachment to the teeth. However, in some instances, it may be desirable or necessary to provide a separate appendage or other anchoring element 1504 on the teeth 1502 using a corresponding receptacle or window 1506 in the appliance 1500 so that the appliance can exert a selected force on the teeth. Inclusion is described in a number of patents and patent applications (including, for example, U.S. Pat. No. 6,450,807 and U.S. Pat. No. 5,975,893) assigned to Align Technology, Inc., and on company websites accessible on the World Wide Web (see, for example, url "invisal. com")An exemplary appliance for use in the system. Examples of tooth attachment attachments suitable for use with orthodontic appliances are also described in patents and patent applications assigned to alli technologies, inc (including, for example, U.S. patent No. 6,309,215 and U.S. patent No. 6,830,450).

Alternatively, where more complex movements or treatment protocols are involved, it may be advantageous to use auxiliary components (e.g., features, fittings, structures, devices, components, etc.) in conjunction with the orthodontic appliance. Examples of such accessories include, but are not limited to, elastic bands, wires, springs, rods, arch expanders, palate expanders, doublets, bite pads, bite ramps, mandibular advancement plates, bite plates, pontics, hooks, brackets, headgear tubes (headgears), springs, baffles (cups), palatal rods, brackets (frames), stapling devices, buccal screens, buccinator bows (buccinator boxes), wire shields (wire shields), lingual flange pads (flanges and pads), lip pads (lip pads) or blocks, protrusions (protuberances), turf (divot), and the like. In some embodiments, the appliances, systems, and methods described herein include improved orthodontic appliances having integrally formed features shaped to couple with, or replace, these ancillary components.

Fig. 15 shows a tooth repositioning system 1510 that includes a plurality of appliances 1512, 1514, 1516. Any of the appliances described herein can be designed and/or provided as part of a set of multiple appliances for use in a tooth repositioning system. Each appliance may be configured such that the tooth receiving cavity has a geometry corresponding to the intermediate or final tooth arrangement for the appliance. The patient's teeth may be repositioned gradually from the initial tooth arrangement toward the target tooth arrangement by placing a series of stepwise position adjustment appliances on the patient's teeth. For example, tooth repositioning system 1510 may include a first appliance 1512 corresponding to an initial tooth arrangement, one or more intermediate appliances 1514 corresponding to one or more intermediate arrangements, and a final appliance 1516 corresponding to a target arrangement. The target tooth arrangement may be a planned final tooth arrangement selected for the patient's teeth at the end of all planned orthodontic treatments. Additionally, the target arrangement may be one of several intermediate arrangements of the patient's teeth during orthodontic treatment, which may include a variety of different treatment conditions including, but not limited to, the following examples: proposed surgery, proper interproximal removal (IPR), scheduled checks, optimal anchor placement, need for palatal expansion, involvement in dental restorations (e.g., inlays, onlays, crowns, bridges, implants, veneers (veneers), etc.), and the like. For example, it should be understood that the target tooth arrangement may be any planned resulting arrangement for the patient's teeth after one or more incremental repositioning stages. Likewise, the initial tooth arrangement may be any initial arrangement of the patient's teeth followed by one or more stepwise repositioning stages.

Fig. 16 illustrates a method 1550 of orthodontic treatment using multiple appliances, according to many embodiments. The method 1550 may be practiced using any of the appliances or appliance groups described herein. In block 1560, a first orthodontic appliance is applied to the patient's teeth to reposition the teeth from the first tooth arrangement to the second tooth arrangement. In block 1570, a second orthodontic appliance is applied to the patient's teeth to reposition the teeth from the second tooth arrangement to a third tooth arrangement. Any suitable number of ordered appliances and any suitable combination of ordered appliances can be used to repeat method 1550 as desired in order to reposition the patient's teeth from the initial arrangement to the target arrangement in a step-wise manner. The appliances may all be generated at the same stage, or in groups or batches (e.g., at the beginning of the treatment, at an intermediate stage of the treatment, etc.), or one appliance may be manufactured at a time and the patient may wear each appliance until the pressure of each appliance against the teeth is no longer felt, or until the maximum amount of tooth movement exhibited for that given stage has been reached. The plurality of appliances (e.g., a set) can be designed and even manufactured before any of the plurality of appliances are worn by the patient. After wearing the appliances for an appropriate period of time, the patient may replace the current appliance with the next appliance in the series until there are no more remaining appliances. These appliances are generally not adhered to the teeth, and the patient can install and replace the appliance (e.g., a patient removable appliance) at any time during the procedure. The final appliance or appliances in the series of appliances may have one or more geometries selected to over-correct the tooth arrangement. For example, one or more of the appliances may have a geometry that will cause movement of individual teeth beyond the arrangement of teeth selected as the "end" if fully realized. Such over-correction may be desirable in order to counteract potential recurrence after the repositioning method is concluded (e.g., to allow individual teeth to move back toward their pre-corrected positions). Overcorrection is also beneficial to increase the rate of correction (e.g., an appliance whose geometry is positioned beyond a desired intermediate or final position may shift each tooth toward that position at a faster rate). In such a case, the use of the appliance may be stopped before the teeth reach the position defined by the appliance. In addition, over-correction may be intentionally applied in order to compensate for any inaccuracies or limitations of the appliance.

The various embodiments of the orthodontic appliances presented herein can be manufactured in a variety of ways. In some embodiments, the orthodontic appliances herein (or portions thereof) can be produced using direct manufacturing techniques such as additive manufacturing techniques (also referred to herein as "3D printing") or subtractive manufacturing techniques (e.g., milling). In some embodiments, direct manufacturing involves forming an object (e.g., an orthodontic appliance or portion thereof) without using a solid template (e.g., a mold, a mask, etc.) to define the object geometry.

In some embodiments, the orthodontic appliances herein can be manufactured using a combination of direct and indirect manufacturing techniques, such that different portions of the appliance can be manufactured using different manufacturing techniques and assembled to form the final appliance. For example, the appliance shell can be formed by indirect manufacturing (e.g., thermoforming), and one or more structures or components described herein (e.g., auxiliary components, power arms, etc.) can be added to the shell by direct manufacturing (e.g., printing onto the shell).

The configuration of the orthodontic appliances herein can be determined according to the patient's treatment regimen, for example, which involves the sequential implementation of multiple appliances for incrementally repositioning teeth. Computer-based therapy planning and/or appliance manufacturing methods may be used to facilitate the design and manufacture of appliances. For example, one or more of the appliance components described herein may be digitally designed and manufactured with the aid of computer-controlled manufacturing equipment (e.g., Computer Numerical Control (CNC) milling, computer-controlled additive manufacturing, such as 3D printing, etc.). The computer-based methods presented herein may improve the accuracy, flexibility, and convenience of appliance manufacturing.

In some embodiments, a computer-based 3D planning/design tool, such as Treat from allin Technology, Inc^TMSoftware may be used to design and manufacture the orthodontic appliances described herein.

Fig. 17 illustrates a method 1800 for designing an orthodontic appliance to be manufactured, according to an embodiment. The method 1800 may be applied to any embodiment of an orthodontic appliance described herein. Some or all of the operations of method 200 may be performed by any suitable data processing system or device (e.g., one or more processors configured with suitable instructions).

In block 1810, a movement path for moving one or more teeth from an initial arrangement to a target arrangement is determined. The initial alignment can be determined from a mold or scan of the patient's teeth or oral tissue, for example, using wax occlusion, direct contact scanning, x-ray imaging, tomography, ultrasound imaging, and other techniques for obtaining information about the position and structure of the teeth, jaw, gums, and other orthodontic-related tissue. From the acquired data, a digital data set may be derived that represents an initial (e.g., pre-treatment) arrangement of the patient's teeth and other tissue. Optionally, the initial digital data set is processed to segment tissue constituents (tissue constraints) from each other. For example, a data structure may be generated that digitally represents individual crowns. Advantageously, a digital model of the entire tooth may be generated, including measured or inferred hidden surfaces and root structures, as well as surrounding bone and soft tissue.

The target arrangement of teeth (e.g., the desired and expected end result of orthodontic treatment) can be received from a clinician in a prescription, can be calculated by basic orthodontic principles, and/or can be computationally inferred from a clinical prescription. With a specification of the desired final position of the teeth and the digital representation of the teeth themselves, the final position and surface geometry of each tooth can be specified to form a complete model of the tooth arrangement at the end of the desired treatment.

Each tooth has both an initial position and a target position, and a movement path can be defined for the movement of each tooth. In some embodiments, these movement paths are configured to move the tooth in the fastest manner with the least amount of round-trip (round-trip) to move the tooth from its initial position to its desired target position. Alternatively, the tooth path can be segmented and the segments can be calculated so that the motion of each tooth within a segment remains within the threshold limits of linear translation and rotational translation. In this way, the end points of each path segment may constitute a clinically viable repositioning, and the collection of segment end points may constitute a clinically viable sequence of tooth positions, such that movement from one point to the next in the sequence does not cause collision of teeth.

In block 1820, a force system for moving one or more teeth along a path of movement is determined. The force train may comprise one or more forces and/or one or more torques. Different force systems cause different types of tooth movement, such as tilting, translation, rotation, extrusion, intrusion, root movement, and the like. Biomechanical principles, modeling techniques, force calculation/measurement techniques, and the like (including knowledge and protocols commonly used in orthodontics) can be used to determine an appropriate force system to be applied to the teeth to effect tooth movement. Resources that may be considered in determining the force system to be applied include literature, force systems determined through experimental or virtual modeling, computer-based modeling, clinical experience, minimizing unwanted forces, and the like.

The force system may be determined in various ways. For example, in some embodiments, the force system is determined on a patient-by-patient basis (e.g., using patient-specific data). Alternatively or in combination, the force system may be determined based on a generalized model of tooth movement (e.g., based on experimentation, modeling, clinical data, etc.), such that patient-specific data is not necessarily used. In some embodiments, the determination of the force system includes calculating specific force values to be applied to one or more teeth to produce a particular movement. In addition, the force system can be determined at a high level without calculating a specific force value of the tooth. For example, block 1820 may include determining a particular type of force to be applied (e.g., an extrusion force, a translation force, a rotational force, a tilt force, a torque force, etc.) without calculating a particular magnitude and/or direction of the force.

In block 1830, an appliance geometry and/or material composition of an orthodontic appliance configured to generate a force train is determined. The orthosis can be any of the embodiments of the orthosis discussed herein, such as an orthosis having variable local properties, integrally formed components and/or powered arms.

For example, in some embodiments, the orthosis comprises a non-uniform (hetereogenous) thickness, a non-uniform stiffness, or a non-uniform material composition. In some embodiments, the orthosis comprises two or more of non-uniform thickness, non-uniform stiffness or non-uniform material composition. In some embodiments, the orthosis comprises a non-uniform thickness, a non-uniform stiffness, and a non-uniform material composition. The non-uniform thickness, stiffness, and/or material composition may be configured to create a force system for moving the teeth, such as by preferentially applying force at certain locations of the teeth. For example, an appliance having a non-uniform thickness may include thicker portions that exert a greater force on the teeth than thinner portions. As another example, an appliance having a non-uniform stiffness may include a stiffer portion that exerts a greater force on the teeth than a more resilient portion. As described herein, variation in stiffness can be achieved by varying the appliance thickness, material composition, and/or degree of photopolymerization.

In some embodiments, determining the geometry and/or material composition of the appliance includes determining the geometry and/or material composition of one or more integrally formed components to be manufactured directly with the appliance shell. The integrally formed component may be any of the embodiments described herein. The geometry and/or material composition of the integrally formed component may be selected to facilitate application of the force train to the patient's teeth. The material composition of the integrally formed component may be the same as or different from the material composition of the housing.

In some embodiments, determining the appliance geometry includes determining a variable roof bend (cable bend) geometry.

Block 1830 may include analyzing the desired force train to determine the appliance geometry and material composition that will produce the force train. In some embodiments, the analysis includes determining an appliance characteristic (e.g., stiffness) at one or more locations that will produce a desired force at the one or more locations. The analysis then includes determining the appliance geometry and material composition at one or more locations to achieve the specified characteristics. The appliance geometry and material composition may be determined using a treatment or force application simulation environment. The simulation environment may include, for example, a computer modeling system, a biomechanical system or an applianceAnd (4) standing and the like. Alternatively, a digital model, such as a finite element model, of the appliance and/or teeth may be made. The finite element model may be created using computer program application software available from various suppliers. To create the solid geometric model, a Computer Aided Engineering (CAE) or Computer Aided Design (CAD) program may be used, such as those available from Autodesk (Autodesk) Inc. of san Lafibei, CanadaA software product. To create finite element models and analyze them, program products from several vendors including the finite element analysis software package from ANSYS corporation of calnsterg, pa and the simulia (abequs) software product from dasotsu sys mes of waltham, ma may be used.

Optionally, one or more appliance geometries and material compositions may be selected for testing or force modeling. As described above, the desired tooth movement and the force train needed or desired to cause the desired tooth movement can be identified. By using a simulated environment, the geometry and composition of the candidate appliance may be analyzed or modeled to determine the actual force system generated by using the candidate appliance. One or more modifications may optionally be made to the candidate appliances and the force modeling (as described) may be further analyzed, e.g., to iteratively determine the appliance design that produces the desired force system.

Optionally, block 1830 may also include determining a geometry of one or more auxiliary components to be used in conjunction with the orthodontic appliance to apply a force train on one or more teeth. These auxiliary devices may include one or more of the following: a dental attachment, elastic band (elastic), wire, spring, bite block, arch expander, wire-bracket appliance, shell appliance, headgear, or any other orthodontic device or system that can be used in conjunction with the orthodontic appliances herein. The use of these auxiliary components may be advantageous in situations where the appliance is difficult to generate the force train alone. In addition, auxiliary components may be added to the orthodontic appliance to provide other desired functions in addition to generating a force train, such as mandibular advancement splints for treating sleep apnea, bridges for improving aesthetic appearance, and the like. In some embodiments, the auxiliary component is manufactured and provided separately from the orthodontic appliance. In addition, the geometry of the orthodontic appliance may be modified to include one or more auxiliary components as integrally formed components.

In block 1840, instructions for manufacturing an orthodontic appliance having an appliance geometry and a material composition are generated. The instructions may be configured to control a manufacturing system or device to produce an orthodontic appliance having a specified appliance geometry and material composition. In some embodiments, the instructions may be configured to manufacture the orthodontic appliance using direct manufacturing (e.g., stereolithography, selective laser sintering, fused deposition modeling, 3D printing, continuous direct manufacturing, multi-material direct manufacturing, etc.). Alternatively, as discussed above and herein, the instructions may be configured to cause a manufacturing machine to directly manufacture an orthodontic appliance having a tooth receiving cavity with a variable roof-like curvature. In alternative embodiments, the instructions may be configured to indirectly manufacture the appliance (e.g., by thermoforming).

While the above-described blocks illustrate a method 1800 of designing an orthodontic appliance according to some embodiments, one of ordinary skill in the art will recognize variations in light of the teachings described herein. Some blocks may include sub-blocks. Some blocks may be repeated as often as necessary. One or more blocks of method 200 may be performed using any suitable manufacturing system or apparatus, such as the embodiments described herein. Some blocks are optional and the order of the blocks may be changed as desired. For example, in some embodiments, block 1820 is optional such that block 1830 includes determining the appliance geometry and/or material composition based directly on the tooth movement path, rather than based on the force system.

Fig. 18 illustrates a method 1900 for digitally planning the design or manufacture of an orthodontic treatment and/or appliance, according to an embodiment. The method 1900 can be applied to any of the treatment procedures described herein and can be performed by any suitable data processing system.

In block 1910, a digital representation of a patient's teeth is received. The digital representation may include surface topography data of the patient's oral cavity, including teeth, gingival tissue, and the like. The surface topography data may be generated by directly scanning the oral cavity, a solid model of the oral cavity (positive or negative), or an impression of the oral cavity using a suitable scanning device (e.g., a handheld scanner, a desktop scanner, etc.).

In block 1920, one or more treatment stages are generated based on the digital representation of the teeth. These treatment stages may be a gradual repositioning stage of an orthodontic treatment procedure designed to move one or more teeth of a patient from an initial tooth arrangement to a target arrangement. These treatment phases may be generated, for example, by determining an initial tooth arrangement indicated by the digital representation, determining a target tooth arrangement, and determining a path of movement of one or more teeth in the initial arrangement required to achieve the target tooth arrangement. The movement path may be optimized based on minimizing the total movement distance, preventing collisions between teeth, avoiding tooth movement that is more difficult to achieve, or any other suitable criteria.

In block 1930, at least one orthodontic appliance is manufactured based on the generated treatment stage. For example, a set of appliances may be manufactured, each shaped according to the tooth arrangement specified for a particular stage of treatment, such that the appliances can be worn by the patient in sequence to reposition the teeth from the initial arrangement to the target arrangement in a step-wise manner. The set of appliances may include one or more of the orthodontic appliances described herein. The manufacture of the appliance may include creating a digital model of the appliance for use as an input to a computer controlled manufacturing system. The orthosis can be formed using a direct manufacturing method, an indirect manufacturing method, or a combination of both, as desired.

In some cases, staging of various permutations or treatment stages may not be necessary for the design and/or manufacture of the appliance. As shown in phantom in fig. 18, the design and/or manufacture of the orthodontic appliance, and possibly the particular orthodontic treatment, may include using a representation of the patient's teeth (e.g., receiving a digital representation 1910 of the patient's teeth), followed by designing and/or manufacturing the orthodontic appliance based on the representation of the patient's teeth in the arrangement represented by the received representation.

Optionally, some or all of the blocks of method 1900 are performed locally, referred to herein as "chair-side manufacturing," at the site where the patient is undergoing treatment and during a visit of the patient. For example, chair-side manufacturing may include scanning a patient's teeth, automatically generating a treatment plan with various treatment stages, and immediately manufacturing one or more orthodontic appliances for treatment of the patient using a chair-side direct manufacturing machine, all during a single appointment, in a professional therapist's office. In embodiments where a series of appliances are used to treat a patient, the first appliance may be fabricated at the chair for immediate delivery to the patient, while the remaining appliances are fabricated separately (e.g., off-site at a laboratory or central manufacturing facility) and delivered at a later time (e.g., in a follow-up appointment, mailed to the patient). In addition, the methods herein can be adapted to produce and deliver an entire series of appliances on-site during a visit. Thus, chair-side manufacturing can improve the convenience and speed of the treatment procedure by starting the treatment for the patient immediately at the physician's office, rather than having to wait for the appliance to be manufactured and delivered at a later date. In addition, chair-side manufacturing may improve the flexibility and efficiency of orthodontic treatment. For example, in some embodiments, the patient is rescanned at each visit to determine the actual position of the teeth and the treatment plan is updated accordingly. New appliances can then be made and delivered immediately at the chair to accommodate any changes or deviations in the treatment plan.

FIG. 19 is a simplified block diagram of a data processing system 2000 that may be used to perform the methods and processes described herein. The data processing system 2000 typically includes at least one processor 2002 that communicates with one or more peripheral devices via a bus subsystem 2004. These peripheral devices typically include a storage subsystem 2006 (memory subsystem 2008 and file storage subsystem 2014), a set of user interface input and output devices 2018, and an interface to an external network 2016. This interface is shown schematically as a "network interface" block 2016 and is coupled to corresponding interface devices in other data processing systems via a communications network interface 2024. Data processing system 2000 may include, for example, one or more computers, such as personal computers, workstations, mainframes, laptops, and the like.

The user interface input devices 2018 are not limited to any particular device and may generally include, for example, a keyboard, pointing device, mouse, scanner, interactive display, touch pad, joystick, or the like. Similarly, various user interface output devices can be used in the systems of the present invention and can include, for example, one or more of a printer, a display (e.g., visual, non-visual) system/subsystem, a controller, a projection device, an audio output, and the like.

The storage subsystem 2006 holds the basic required programming, including computer-readable media having instructions (e.g., operational instructions, etc.) and data structures. Program modules discussed herein are typically stored in the storage subsystem 2006. The storage subsystem 2006 typically includes a memory subsystem 2008 and a file storage subsystem 2014. Memory subsystem 2008 typically includes a number of memories (e.g., RAM 2010, ROM 2012, etc.) including a computer readable memory for storing fixed instructions, instructions and data during program execution, a basic input/output system, and the like. The file storage subsystem 2014 permanently (non-volatile) stores program and data files, and may include one or more removable or fixed drives or media, hard disks, floppy disks, CD-ROMs, DVDs, optical disk drives, or the like. One or more of the storage systems, drives, etc. may be located at a remote location, e.g., coupled through a server on a network or through the internet/world wide web. In this context, the term "bus subsystem" is generally used to include any mechanism for bringing various components and subsystems into desired communication with one another, and may include various suitable components/systems that may be considered or considered suitable for use therein. It should be appreciated that the various components of the system may, but need not, be in the same physical location, but may be connected via various local or wide area network media, transmission systems, and the like.

Scanner 2020 includes any means for obtaining a digital representation (e.g., an image, topographical data, etc.) of a patient's teeth (e.g., by scanning a physical model of the teeth such as cast 2021, by scanning an impression of the teeth, or by directly scanning an oral cavity), the digital representation being available from the patient or a treating professional (e.g., an orthodontist), and scanner 2020 further includes means for providing the digital representation to data processing system 2000 for further processing. The scanner 2020 may be located at a remote location relative to the other components of the system and may transmit image data and/or information to the data processing system 2000, for example, via the network interface 2024. The manufacturing system 2022 manufactures the appliances 2023 based on a treatment plan that includes data set information received from the data processing system 2000. For example, the manufacturing machine 2022 can be located at a remote location and receive data set information from the data processing system 2000 via the network interface 2024. The camera 2025 may include any image capture device configured to capture still images or movies. The camera 2025 may capture various perspectives of the patient's dentition. In some implementations, the camera 2025 can capture images at different focal lengths at different distances from the patient.

The data processing aspects of the methods described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in suitable combinations of them. The data processing apparatus can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor. The data processing blocks may be executed by a programmable processor executing program instructions to perform functions by operating on input data and generating output. The data processing aspects can be implemented on one or more computer programs that are executable on a programmable system including one or more programmable processors operatively coupled to a data storage system. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, such as: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and a CD-ROM disk.

Although the detailed description contains many specifics, these specifics should not be construed as limiting the scope of the disclosure, but merely as illustrating different examples and aspects of the disclosure. It should be understood that the scope of the present disclosure includes other embodiments not discussed in detail above. Various other modifications, changes, and variations which will be apparent to those skilled in the art may be made in the arrangement, operation, and details of the methods, systems, and apparatus of the present disclosure provided herein without departing from the spirit and scope of the invention described herein.

The terms "dental appliance" and "tooth receiving appliance" as used herein are considered synonymous. As used herein, "dental positioning appliance" or "orthodontic appliance" may be considered synonymous and may include any dental appliance configured to change the position of a patient's teeth according to a regimen, such as an orthodontic treatment regimen. As used herein, "patient" may include any person, including persons seeking dental/orthodontic treatment, persons receiving dental/orthodontic treatment, and persons who have previously received dental/orthodontic treatment. A "patient" may include a customer or potential customer of orthodontic treatment, such as a person who uses the visualization tools herein to inform him of a decision to fully accept orthodontic treatment or a decision to select a particular orthodontic treatment regimen. As used herein, a "dental positioning appliance" or "orthodontic appliance" may include a set of dental appliances configured to gradually change the position of a patient's teeth over time. As described herein, a dental positioning appliance and/or orthodontic appliance may include a polymeric appliance configured to move a patient's teeth according to an orthodontic treatment protocol.

As used herein, the term "and/or" may be used as a functional word to indicate that two words or phrases are made together or separately. For example, the word "A and/or B" includes A alone, B alone, and A and B together. Depending on the context, the term "or" does not exclude one of a plurality of words/phrases. For example, the phrase "a or B" does not exclude a and B together.

The terms "torque" and "moment" as used herein are considered synonymous.

As used herein, "moment" may include a force acting on an object, such as a tooth, at a distance from the center of the impedance. For example, the moment may be calculated as the vector product of the vector forces applied to a location corresponding to the displacement vector from the center of the impedance. The moment may comprise a vector pointing in one direction. The moment opposite the other moment may comprise one of a moment vector towards a first side of the object, such as a tooth, and another moment vector towards an opposite side of the object, such as a tooth. Any discussion herein regarding the application of force to a patient's teeth is equally applicable to the application of torque to the teeth, and vice versa.

As used herein, "plurality of teeth" may include two or more teeth. The plurality of teeth may include adjacent teeth, but need not include adjacent teeth. In some embodiments, the one or more posterior teeth comprise one or more of molars, premolars, or canines, and the one or more anterior teeth comprise one or more of central incisors, lateral incisors, canines, first bicuspids, or second bicuspids.

Embodiments disclosed herein may be well suited for moving one or more teeth of a first set of one or more teeth or moving one or more teeth of a second set of one or more teeth, and combinations thereof.

The embodiments disclosed herein may be well suited for incorporation with one or more commercially available tooth moving components (e.g., attachments and polymeric shell appliances). In some embodiments, the appliance and one or more attachments are configured to move the one or more teeth along a tooth movement vector comprising six degrees of freedom, three degrees of freedom being rotational degrees of freedom and three degrees of freedom being translational degrees of freedom.

Repositioning of teeth may be accomplished using a series of removable elastic positioning appliances, such as those available from Align Technology, Inc., the assignee of the present disclosureProvided is a system. These appliances may have a thin shell of resilient material that generally conforms to the patient's teeth but is slightly misaligned with the initial or immediately preceding tooth arrangement. Placing the appliance over the teeth can apply controlled forces at specific locations to gradually move the teeth into the new configuration. Repeating this process with successive appliances containing the new configuration eventually moves the teeth through a series of intermediate configurations or alignment patterns to the final desired configuration. Repositioning of teeth may be accomplished by other series of removable orthodontic appliances and/or dental appliances, including polymeric shell appliances.

The computer system used herein is intended to be interpreted broadly. Generally, a computer system includes a processor, memory, non-volatile memory, and an interface. A typical computer system will usually include at least a processor, memory, and a device (e.g., a bus) coupling the memory to the processor. For example, the processor may be a general purpose Central Processing Unit (CPU) such as a microprocessor or a special purpose processor such as a microcontroller.

By way of example, and not limitation, memory may include Random Access Memory (RAM), such as dynamic RAM (dram) and static RAM (sram). The memory may be local, remote, or distributed. The bus may also couple the processor to non-volatile memory. The non-volatile memory is typically a magnetic floppy disk or magnetic hard disk, a magneto-optical disk, an optical disk, a Read Only Memory (ROM) such as a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or other forms of memory for large amounts of data. During execution of software on a computer system, some of this data is typically written to memory through a direct memory access process. The non-volatile memory may be local, remote, or distributed. Non-volatile memory is optional, as the system can be created with all applicable data available in memory.

The software is typically stored in non-volatile memory. In fact, for large programs, it may even be impossible to store the entire program in memory. Nevertheless, it will be understood that in order for the software to be able to run, it is moved, if necessary, to a computer readable location suitable for processing, and for purposes of illustration, this location is referred to herein as memory. Even when software is moved to memory for execution, processors typically utilize hardware registers to store values associated with the software, and ideally local caches to speed up execution. As used herein, when a software program is referred to as being "embodied in a computer-readable storage medium," the software program is assumed to be stored in a known or convenient location (from non-volatile memory to hardware registers) where applicable. A processor is said to be "configured to execute a program" when at least one value associated with the program is stored in a register readable by the processor.

In one example of operation, a computer system may be controlled by operating system software, which is a software program that includes a file management system, such as a disk operating system. One example of operating system software with associated file management system software is Microsoft corporation, Redmond, WashingtonBut the known family of operating systems and their associated file management systems. Another example of operating system software having its associated file management system software is the Linux operating system and its associated file management system. The file management system is typically stored in non-volatile memory and causes the processor to perform various actions required by the operating system to input and output data and store the data in memory, including storing files on non-volatile memory。

The bus may also couple the processor to the interface. An interface may include one or more input and/or output (I/O) devices. Depending on implementation-specific considerations or other considerations, by way of example and not limitation, I/O devices may include keyboards, mice or other pointing devices, disk drives, printers, scanners, and other I/O devices including display devices. By way of example and not limitation, the display device may include a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), or some other suitable known or convenient display device. The interface may include one or more of a modem or a network interface. It will be appreciated that a modem or network interface may be considered part of the computer system. The interfaces may include analog modems, ISDN modems, cable modems, token ring interfaces, satellite transmission interfaces (e.g., "direct PC"), or other interfaces for coupling a computer system to other computer systems. The interfaces enable the computer system and other devices to be coupled together in a network.

The computer system may be compatible with or implemented as part of a cloud-based computing system or implemented by a cloud-based computing system. As used herein, a cloud-based computing system is a system that provides virtualized computing resources, software, and/or information to end-user devices. Computing resources, software, and/or information may be virtualized by maintaining centralized services and resources that may be accessed by edge devices through a communication interface (e.g., a network). A "cloud" may be a marketing term and, for purposes herein, may include any of the networks described herein. The cloud-based computing system may involve a subscription service or use a utility price model. A user may access the protocols of the cloud-based computing system through a web browser or other container application located on their end user device.

The computer system may be implemented as an engine, a portion of an engine, or by multiple engines. As used herein, an engine includes one or more processors or a portion of one or more processors. A portion of one or more processors may include some portion of hardware, rather than all hardware, including any given one or more processors, e.g., a subset of registers, a portion of a processor dedicated to one or more threads of a multithreaded processor, a time period during which a processor is dedicated, in whole or in part, to performing a portion of the functions of an engine, etc. Thus, the first and second engines may have one or more dedicated processors, or the first and second engines may share one or more processors with each other or with other engines. Depending on implementation-specific considerations or other considerations, the engines may be centralized or their functionality distributed. The engine may comprise hardware, firmware, or software embodied in a computer readable medium for execution by a processor. For example, as described with reference to the figures herein, a processor converts data into new data using implemented data structures and methods.

The engines described herein or the engines that may implement the systems and devices described herein may be cloud-based engines. As used herein, a cloud-based engine is an engine that can run applications and/or functionality using a cloud-based computing system. All or a portion of the applications and/or functionality may be distributed across multiple computing devices and need not be limited to only one computing device. In some embodiments, the cloud-based engine may execute functions and/or modules accessed by an end user through a web browser or container application without having to install the functions and/or modules locally on the end user's computing device.

As used herein, a data warehouse is intended to include a repository having any suitable organization of data, including tables, Comma Separated Value (CSV) files, traditional databases (e.g., SQL), or other suitable known or convenient organizational formats. For example, the data store may be implemented as software embodied in a physical computer-readable medium on a special purpose machine, in firmware, in hardware, in a combination thereof, or in a suitable known or convenient device or system. A data warehouse-related component (e.g., a database interface) may be considered "part of" a data warehouse, part of some other system component, or a combination thereof, although the physical location and other characteristics of the data warehouse-related component are not critical to understanding the techniques described herein.

The data warehouse may include a data structure. As used herein, a data structure is associated with a particular way of storing and organizing data in a computer, thereby enabling it to be utilized efficiently in a given context. Data structures are typically based on the ability of a computer to retrieve data located anywhere in its memory and to store data anywhere in its memory, the location being specified by an address, which is a string of bits that can be stored in memory itself and manipulated by a program. Thus, some data structures are based on computing addresses of data items using arithmetic operations; while other data structures are based on storing the address of the data item in the structure itself. Many data structures use both principles simultaneously, sometimes in a non-trivial way. Implementation of a data structure typically involves writing a set of programs that create and manipulate instances of the structure. The data warehouse described herein may be a cloud-based data warehouse. Cloud-based data warehouse refers to a data warehouse that is compatible with cloud-based computing systems and engines.

Although reference is made to appliances that include polymer shell appliances, the embodiments disclosed herein are well suited for use with many appliances that receive teeth (e.g., appliances that do not have one or more of a polymer or shell). For example, the orthosis may be fabricated from one or more of a number of materials, such as metal, glass, reinforced fibers, carbon fibers, composites, reinforced composites, aluminum, biomaterials, and combinations thereof. For example, the orthosis may be shaped in a number of ways, for example by thermoforming or direct manufacture as described herein. Additionally or in combination, the orthosis may be manufactured by machining, for example by computer numerically controlled machining, from a block of material. Additionally, although reference is made herein to orthodontic appliances, at least some of the techniques described herein may be applicable to prosthetic appliances and/or other dental appliances, including but not limited to crowns, dental decorations, tooth whitening appliances, tooth protection appliances, and the like.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

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