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 system includes one or more processors coupled to non-transitory memory, and the one or more processors are configured to receive a first image representing at least a portion of a mouth of a user, execute a first machine-learning architecture trained to generate a set of features from the first image, determine, based on the set of features, that the first image satisfies at least one criteria for executing a second machine-learning architecture based on the first image, and generate, based on the first image satisfying the at least one criteria, a prompt indicating feedback for capturing a second image representing at least a second portion of the mouth of the user.

Abstract: Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Abstract: A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

Abstract: Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Abstract: Disclosed is a machine learning architecture for a two-dimensional image protocol detector configured to receive a first image representing at least a portion of a mouth of a user, and output user feedback for capturing a second image representing a portion of the mouth of the user, where the machine learning architecture outputs the user feedback in response to an image quality score of the first image not satisfying an image quality threshold.