Artificial Intelligence System For Orthodontic Measurement, Treatment Planning, And Risk Assessment

摘要

A first machine learning model (e.g., GAN) is trained to take as inputs a dental image and masks of dental features in the image and outputs a set of orthodontic points. A second machine learning model may additionally take the orthodontic points as inputs and output distances between the orthodontic points. A third machine learning model may additionally take the orthodontic points and distances as inputs and output a deformation vector field for the orthodontic points. A fourth machine learning model may additionally take the orthodontic points as inputs and generate a vector indicating risk associated with orthodontic treatment. A fifth machine learning model may additionally take the orthodontic points, deformation vector field, and distances as inputs and output a treatment plan, including point clouds for brackets, retainers, appliances, mandibular surgery or movement, and/or maxillary surgery or movement.