Artificially intelligent systems to manage virtual dental models using dental images

A system comprising:

• one or more processors;
• memory storing computer-program instructions that, when executed by the one or more processors cause the system to implement a method comprising:
• accessing a plurality of first two-dimensional (2D) images, wherein the plurality of first 2D images:
• represents a subject's oral cavity, each has first areas that can be segmented into a plurality of dental classes, each has a first relationship to a first three-dimensional (3D) model of the subject's oral cavity, and each has first height map data representing distances between the subject's oral cavity and an image capture device;
• accessing one or more automated machine learning agents trained to modify to one or more second 3D models into the plurality of dental classes, the trained modifications using second height map data of a plurality of second 2D images and further using second relationships between the plurality of second 2D images and the one or more second 3D models;
• instructing the one or more automated machine learning agents to use the first height map data to modify the first areas of the plurality of first 2D images to get a plurality of modified first 2D images;
• and
• using the first relationships between the plurality of first 2D images and the first 3D models, and using the plurality of modified first 2D images to modify first mesh regions of the first 3D model corresponding to the first areas of the plurality of first 2D images.

A method comprising:

• gathering a plurality of first two-dimensional (2D) images, wherein the plurality of first 2D images:
• represents a subject's oral cavity, each has first areas that can be segmented into a plurality of dental classes, each has first projection values in relation to a first three-dimensional (3D) model of the subject's oral cavity, and each has first height map data representing distances between the subject's oral cavity and an image capture device;
• accessing one or more automated machine learning agents trained to segment one or more second 3D models into the plurality of dental classes, the trained segmenting using second height map data of a plurality of second 2D images and further using second projection values relating the plurality of second 2D images to the one or more second 3D models;
• instructing the one or more automated machine learning agents to use the first height map data to segment the first areas of the plurality of first 2D images into the plurality of dental classes to get a plurality of segmented first 2D images;
• and
• using the first projection values and the plurality of segmented first 2D images to segment the first 3D model of the subject's oral cavity into the plurality of dental classes.