Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.

Abstract: Systems and methods disclosed herein include a processor and a non-transitory computer readable medium containing instructions that when executed by the processor causes the processor to receive an image representing a first portion of a mouth of a user, segment the image to generate segmented regions of teeth present in the image, generate an imaging record by mapping the segmented regions of teeth present in the image to a template model where the imaging record indicates regions of the teeth that remain to be captured, and provide feedback identifying the regions of the teeth that remain to be captured based on the imaging record.

Abstract: A method to generate a photo realistic dentition image includes receiving a 2D image of a patient dentition including a plurality of patient teeth. The method includes obtaining a template dentition including a plurality of template teeth that correspond with a subset of the patient teeth. The method includes generating a tooth location input to position the plurality of template teeth relative to the subset of the plurality of patient teeth. The method includes identifying an area of generation in the 2D image including a plurality of pixels and contextual information. The method includes generating new photo realistic teeth based on the tooth location input, the contextual information, and the area of generation. The method includes disposing the photo realistic teeth in the 2D image.

Abstract: Techniques for determining a presence of a pathology property in a supra-image are presented. The techniques can include receiving an electronic evaluation supra-image; providing the electronic evaluation supra-image to an electronic neural network that has been trained, using a training corpus of training supra-images and on an electronic computer, to determine the presence of the pathology property in a supra-image, each training supra-image including at least one image, each image corresponding to a plurality of components, wherein each training supra-image of the training corpus is associated with a respective electronic label indicating whether the pathology property is present, where the training corpus is sufficient to train the electronic neural network to determine a presence of the pathology property; receiving from the electronic neural network an output indicative of whether the pathology property is present in the evaluation supra-image; and providing the output.

Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.