Coordinate System Prediction in Digital Dentistry and Digital Orthodontics, and the Validation of that Prediction
AssigneeSolventum Intellectual Properties Company
InventorsSeyed Amir Hossein Hosseini, Jonathan D. Gandrud, Marie D. Manner, Joseph C. Dingeldein, Wenbo Dong
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Abstract
Systems and techniques for training one or more encoders to automatically generate coordinate systems used in digital dentistry are disclosed including predicting one or more predicted transformations pertaining to one or more coordinate axes, determining a loss value that specifies a difference between the one or more predicted transformations and one or more respective reference transformations and modifying at least one aspect of the encoder structure based on the loss.
Claims20
A computer-implemented method for training one or more neural networks to automatically generate coordinate systems used in digital oral care, the method comprising:
- receiving, by one or more computer processors, a first digital 3D oral care representation of a patient's teeth;
- receiving, by the one or more computer processors, one or more reference coordinate axes in proximity to one or more teeth in the first 3D oral care representation;
- using, by the one or more computer processors, a first configuration of one or more neural networks that have been initially trained to generate a modified representation of the first digital 3D oral care representation;
- using, by the one or more computer processors, a second configuration of one or more neural networks that have been initially trained to predict information pertaining to one or more coordinate axes and wherein the second configuration receives as input the modified representation generated by the first configuration;
- automatically training, by the one or more computer processors, the second configuration, based on using the second configuration, wherein the training of the second configuration is modified by performing operations comprising:
- predicting, by the second configuration, one or more predicted one or more directional vectors pertaining to the one or more coordinate axes;
- computing, by the one or more computer processors, one or more predicted transformations from the one or more directional vectors;
- determining, by the one or more computer processors, a loss value that specifies a difference between the one or more predicted transformations and the one or more respective reference transformations;
- and
- modifying at least one aspect of the one or more neural networks included in the second configuration based on the loss value.
A system comprising:
- one or more computer processors;
- non-transitory computer-readable storage having stored thereon first and second configurations of one or more neural networks and instructions that when executed by the one or more processors cause the one or more processors to:
- receive a first digital 3D oral care representation of a patient's teeth;
- receive one or more coordinate axes in proximity to one or more teeth in the first 3D oral care representation;
- use the first configuration of one or more neural networks that have been initially trained to generate a modified representation of the first digital 3D oral care representation;
- use the second configuration of one or more neural networks that have been initially trained to predict information pertaining to the one or more coordinate axes and wherein the second configuration receives as input the modified representation generated by the first configuration;
- automatically train the second configuration, based on using the second configuration, wherein the training of the second configuration is modified by performing operations comprising:
- predict, using the second configuration, one or more predicted transformations pertaining to the one or more coordinate axes;
- determine a loss value that specifies a difference between the one or more predicted transformations and one or more respective reference transformations;
- and
- modify at least one aspect of the one or more neural networks included in the second configuration based on the loss value.
Dependencies are inferred from the claim text.