Abstract: Disclosed herein is a dental method that comprises receiving a selection of a replacement tooth for a subject. The method further comprises receiving one or more three-dimensional digital tooth models descriptive of one or more teeth of the subject. The method further comprises receiving a generated three-dimensional digital model of the replacement tooth in response to inputting the one or more three-dimensional digital tooth models descriptive of one or more teeth of the subject into a tooth model generating neural network. The tooth model generating neural network comprises an encoder portion and a decoder portion. The encoder portion is configured for outputting a collective feature vector descriptive of the one or more digital tooth models. The tooth model generating neural network further comprises at least one fully connected layer configured to output a latent space vector into the decoder portion in response to receiving the collective feature vector.

Abstract: Aspects of the disclosed technology provide solutions of simulating patient treatment outcomes and in particular, for estimating treatment parameters that can be optimally modified to achieve a desired treatment outcome. In some aspects, a process of the disclosed technology includes steps for receiving patient data, receiving a plurality of treatment parameters, and generating an output state of the patient based on the initial state of the patient and the treatment goal for the patient. In some aspects, the process can further include steps for calculating an objective function based on the output state of the patient and the treatment goal of the patient, and updating one or more of the treatment parameters based on the objective function. Systems and machine-readable media are also provided.

Abstract: A method includes receiving a) orthognathic patient data comprising information representing an initial state of a patient and b) plurality of treatment parameters that are based on a treatment goal for the patient. The method further includes generating an output state of the patient based on the initial state of the patient and the plurality of treatment parameters, calculating an objective function based on the output state of the patient and the treatment goal of the patient, and updating one or more of the treatment parameters based on the objective function.