Geometric Deep Learning for Setups and Staging in Clear Tray Aligners
AssigneeSolventum Intellectual Properties Company
InventorsJonathan D. Gandrud, Seyed Amir Hossein Hosseini, Wenbo Dong
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Abstract
Systems and techniques are described for training and using a generative adversarial network (GAN) to produce intermediate stages and final setups for clear tray aligners (CTAs) including receiving, by one or more computer processors, a first digital representation of a patient's teeth, using, by the one or more computer processors and to determine a prediction for one or more tooth movements, a generator that is a neural network included in a GAN and that has been trained to predict one or more tooth movements, and producing, by the one or more processors, an output state that includes at least one of a final setup and one or more intermediate stages.
Claims20
A computer-implemented method for generating setups for orthodontic alignment treatment, comprising:
- receiving, by one or more computer processors, a first digital representation of a patient's teeth, wherein the first digital representation includes a plurality of mesh elements and a respective mesh element feature vector associated with each mesh element in the plurality of mesh elements;
- using, by the one or more computer processors and to determine a prediction for one or more tooth movements for a setup, a generator that comprises one or more neural networks and that has been initially trained to predict one or more tooth movements for a setup;
- further training, by the one or more computer processors, the generator based on the using, wherein the training of the neural network is modified by performing operations comprising:
- predicting, by the generator, one or more tooth movements for a setup based on the first digital representation of the patient's teeth, wherein the one or more tooth movements are described by at least one of a position and an orientation;
- quantifying, by the generator, the difference between a representation of the one or more tooth movements predicted by the generator and a representation of one or more reference tooth movements;
- generating a loss value based on the quantifying;
- and
- modifying the generator based at least in part on the loss value.
A system comprising:
- one or more computer processors;
- non-transitory computer readable storage having stored thereon a generator that comprises one or more neural networks and that has been initially trained to predict one or more tooth movements for a setup, and instructions that when executed by the one or more processors cause the one or more processors to:
- receive a first digital representation of a patient's teeth, wherein the first digital representation includes a plurality of mesh elements and a respective mesh element feature vector associated with each mesh element in the plurality of mesh elements;
- use, to determine a prediction for one or more tooth movements for a setup, the generator;
- further train the generator based on the using, wherein the training of the neural network is modified by performing operations comprising:
- predict, by the generator, one or more tooth movements for a setup based on the first digital representation of the patient's teeth, wherein the one or more tooth movements are described by at least one of a position and an orientation;
- quantify, by the generator, the difference between a representation of the one or more tooth movements predicted by the generator and a representation of one or more reference tooth movements;
- generate a loss value based on the quantifying;
- and
- modify the generator based at least in part on the loss value.
Dependencies are inferred from the claim text.