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US 11,900,538 B2Granted
Systems and methods for constructing a dental arch image using a machine learning model
AssigneeSDC U.S. SmilePay SPV
InventorsJordan Katzman, Christopher Yancey
Life of this patent
Estimating the term…
Abstract
A method includes receiving, by a model generation engine, an image of a dental arch of a user, executing, by the model generation engine, a machine learning model that is trained to receive the image and output a representation of the image, where in at least one iteration during training, the machine learning model determines a difference between one or more features extracted from an iteration of the representation of the image and a corresponding one or more features extracted from the image, and the method further includes outputting, by the model generation engine, an output representation of the image.
Claims30
A method comprising:
- receiving, by a model generation engine, a two-dimensional image of a dental arch of a user;
- executing, by the model generation engine, a machine learning model that is trained to receive the two-dimensional image as input and output a three-dimensional representation of the two-dimensional image, wherein in at least one iteration during training, the model generation engine:
- extracts one or more features defining at least a tooth location of a tooth of the dental arch of the user from an iteration of the three-dimensional representation of the two-dimensional image generated using the machine learning model;
- and
- determines a loss based on the one or more features extracted from the iteration of the three-dimensional representation of the two-dimensional image and a corresponding one or more features extracted from the two-dimensional image;
- and
- outputting, by the model generation engine, an output three-dimensional representation of the two-dimensional image responsive to executing the machine learning model using the two-dimensional image of the dental arch as input.
4 more elements
A method comprising:
- identifying, by an image detector based on a two-dimensional image of a dental arch of a user, one or more features in a portion of a plurality of portions of the two-dimensional image;
- extracting, by a model generation engine, one or more features defining at least a tooth location of a tooth of the dental arch of the user from an iteration of a three-dimensional representation of the two-dimensional image, the three-dimensional representation of the two-dimensional image generated by executing a machine learning model based on the two-dimensional image;
- updating, by the model generation engine, the machine learning model based on a loss calculated based on the one or more features in the portion of the plurality of portions of the two-dimensional image, a corresponding probability of the one or more features, and the one or more features extracted from the iteration of the three-dimensional representation of the two-dimensional image;
- and
- outputting, by the model generation engine, the three-dimensional representation of the two-dimensional image based on the updated model.
2 more elements
A system comprising:
- a processing circuit comprising a processor communicably coupled to a non-transitory computer readable medium, wherein the processor is configured to execute instructions stored on the non-transitory computer readable medium that cause the processor to:
- receive a two-dimensional image of a dental arch of a user;
- execute a machine learning model that is trained to receive the two-dimensional image as input and output a three-dimensional representation of the two-dimensional image, wherein in at least one iteration during training, the processing circuit:
- extracts one or more features defining at least a tooth location of a tooth of the dental arch of the user from an iteration of the three-dimensional representation of the two-dimensional image generated using the machine learning model;
- and
- determines a loss based on the one or more features extracted from the iteration of the three-dimensional representation of the two-dimensional image and a corresponding one or more features extracted from the two-dimensional image, wherein the one or more features extracted from the iteration of the three-dimensional representation of the two-dimensional image correspond to one or more features having a probability of being present in the two-dimensional image and the one or more features extracted from the two-dimensional image correspond to the one or more features having a probability of being present in the two-dimensional image;
- and
- output an output three-dimensional representation of the two-dimensional image responsive to executing the machine learning model using the two-dimensional image of the dental arch as input.
5 more elements
Dependencies are inferred from the claim text.