DEEP LEARNING FOR GENERATING INTERMEDIATE ORTHODONTIC ALIGNER STAGES
Methods for generating intermediate stages for orthodontic aligners using machine learning or deep learning techniques. The method receives a malocclusion of teeth and a planned setup position of the teeth. The malocclusion can be represented by translations and rotations, or by digital 3D models. The method generates intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods. The intermediate stages can be used to generate setups that are output in a format, such as digital 3D models, suitable for use in manufacturing the corresponding aligners.
1 . A method for generating intermediate stages for orthodontic aligners, comprising steps of performed by a processor:
receiving a malocclusion of teeth and a planned setup position of the teeth;
generating intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods; and
outputting the intermediate stages.
2 . The method of claim 1 , wherein the receiving step comprises receiving translations and rotations of teeth for the malocclusion.
3 . The method of claim 1 , wherein the receiving step comprises receiving a digital 3D model for the malocclusion.
4 . The method of claim 1 , wherein the receiving step comprises receiving a final stage for the planned setup position.
5 . The method of claim 1 , wherein the outputting step comprises outputting the intermediate stages as digital 3D models.
6 . The method of claim 1 , wherein the generating step comprises using a multilayer perceptron to generate the intermediate stages.
7 . The method of claim 1 , wherein the generating step comprises using a time series forecasting approach to generate the intermediate stages.
8 . The method of claim 1 , wherein the generating step comprises using a generative adversarial network to generate the intermediate stages.
9 . The method of claim 1 , wherein the generating step comprises using video interpolation models to generate the intermediate stages.
10 . The method of claim 1 , wherein the generating step comprises using a seq2seq model to generate the intermediate stages.
11 . The method of claim 1 , wherein the generating step comprises using a dual arch method to generate the intermediate stages.
12 . The method of claim 1 , further comprising performing post-processing of one or more of the intermediate stages.
13 . The method of claim 12 , wherein the post-processing step comprises resetting fixed teeth for the intermediate stages.
14 . The method of claim 12 , wherein the post-processing step comprises removing collisions between teeth for the intermediate stages.
15 . The method of claim 1 , wherein:
the generating step comprises generating intermediate stages for a particular point in treatment by at least two different deep learning methods; and
the outputting step comprises displaying the intermediate stages for the particular point in treatment.
16 . The method of claim 15 , wherein the displaying step comprises displaying the intermediate stages for the particular point in treatment side-by-side within a user interface.
17 . A system for generating intermediate stages for orthodontic aligners, comprising a processor configured to:
receive a malocclusion of teeth and a planned setup position of the teeth;
generate intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods; and
output the intermediate stages.
18 . The system of claim 17 , wherein to receive the malocclusion, the processor is configured to receive translations and rotations of teeth for the malocclusion.
19 . The system of claim 17 , wherein to receive the malocclusion, the processor is configured to receive a digital 3D model for the malocclusion.
20 . The system of claim 17 , wherein to receive the malocclusion, the processor is configured to receive a final stage for the planned setup position.