Abstract: Disclosed is a computer-implemented method that includes receiving, by one or more processors, a three-dimensional (3D) representation of a user's mouth where the 3D representation is a mesh representation comprising a plurality of surfaces; mapping, by the one or more processors, the 3D representation of the user's mouth into a two-dimensional (2D) space; encoding, one or more 3D surface characteristics of the plurality of surfaces using one or more channels of a 2D representation; applying, by the one or more processors, a machine learning model to the representation of the user's mouth in 2D space, the machine learning model trained to enhance the representation of the user's mouth in 2D space; and mapping, by the one or more processors, the enhanced representation of the user's mouth in 2D space to an enhanced representation of the user's mouth in 3D space using the one or more surface characteristics.

Abstract: A mobile device comprises a processing circuit to capture a two-dimensional (2D) representation representing one or more teeth of a user using a camera where the user is a patient seeking treatment, generate a three-dimensional (3D) representation of the one or more teeth based on the 2D representation using an artificial intelligence model where the 3D representation depicts the one or more teeth of the user in a final position, display a graphical visualization including the 3D representation where the graphical visualization is configured to enable the user to make a selection, and causing an order for at least one dental aligner for repositioning the one or more teeth of the user according to a treatment plan to a position corresponding with the final position to be transmitted over a cellular network to a fabrication system where the order initiates a payment using payment information of the user.

Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.

Abstract: Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition. The dentition includes a model tooth in a first position. The method includes receiving a digital representation comprising a patient tooth. The patient tooth corresponds to the model tooth of the first 3D model. The method includes determining a virtual camera parameter associated with the digital representation. The method includes adjusting the virtual camera parameter to determine a 3D position of the corresponding patient tooth. The method includes moving the model tooth of the first 3D model from the first position to a second position. The second position corresponds to the 3D position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.

Abstract: Systems and methods for identifying tooth keypoints are disclosed. A method includes receiving digital representations comprising patient teeth, and identifying a first tooth outline from a first digital representation and a second tooth outline from a second digital representation. The method includes retrieving a 3D mesh comprising model teeth. The method includes projecting a first mesh boundary onto a patient tooth and modifying the first mesh boundary to match the first tooth outline. The method includes identifying a first tooth point on the first tooth outline that corresponds with a first mesh point on the first mesh boundary. The method includes mapping the first tooth point to the 3D mesh. The method includes determining that the first and second tooth points correspond to a common 3D mesh point. The method includes designating the first tooth point as a keypoint. The method includes modifying a digital representation based on the keypoint.

Abstract: Systems and methods for identifying tooth correspondences are disclosed. A method includes receiving a digital representation including patient teeth, and identifying a tooth outline from the digital representation. The method includes retrieving a 3D mesh including model teeth. The method includes projecting a first mesh boundary onto a patient tooth and modifying the first projected mesh boundary to match the first tooth outline. The method includes identifying a first tooth point on the first tooth outline that corresponds with a first mesh point on the first projected mesh boundary. The method includes mapping the first tooth point to the 3D mesh. The method includes determining that the first and second tooth points correspond to a common 3D mesh point. The method includes modifying at least one of the digital representation or the 3D mesh based on determining the tooth points correspond to the common 3D mesh point.

Abstract: Systems and methods for generating a user skeletal model are disclosed herein. A method includes receiving, by one or more processors, a digital representation of a user. The digital representation comprises at least one of a 2D image or a video. The method includes retrieving, by the one or more processors, a template skeletal model. The method includes adjusting, by the one or more processors, the template skeletal model to match a profile of the user depicted in the digital representation. The method includes generating, by the one or more processors, a user skeletal model that resembles a skeleton of the user.

Abstract: Provide are systems methods and devices for diagnosing disease in medical images.

Abstract: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.

Abstract: Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition. The dentition includes a model tooth in a first position. The method includes receiving a digital representation comprising a patient tooth. The patient tooth corresponds to the model tooth of the first 3D model. The method includes determining a virtual camera parameter associated with the digital representation. The method includes adjusting the virtual camera parameter to determine a 3D position of the corresponding patient tooth. The method includes moving the model tooth of the first 3D model from the first position to a second position. The second position corresponds to the 3D position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

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: A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.

Abstract: A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.

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 one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to receive a two-dimensional (2D) representation provided by a user device where the 2D representation represents one or more teeth of a user, generate a three-dimensional (3D) representation of the one or more teeth of the user based on the 2D representation, generate a treatment plan for moving the one or more teeth of the user based on the 3D representation, and provide a graphical visualization of the treatment plan to the user device, wherein the graphical visualization is configured to enable the user to make a selection.

Abstract: Systems and methods for determining movement of teeth include receiving a first 3D representation of a dentition comprising a plurality of teeth of a patient in an initial position, the 3D representation comprising a plurality of tooth representations including a plurality of points representing surfaces of a respective tooth of the dentition, generating, for each tooth representation, a compressed tooth representation using a geometric encoder model trained to compress the 3D representation of the detention, determining tooth movements of the plurality of teeth of the dentition from the initial position to a final position by applying each compressed tooth representation to a final position model trained to output movement of teeth of a dentition from initial positions to final positions, generating another 3D representation of the dentition comprising the plurality of teeth of the patient in the final position based on applying the tooth movements to the first 3D representations.

Abstract: Systems, methods, and computer readable medium of visualizing a treatment of teeth includes capturing, by a user device, a representation representing one or more teeth of a user via a camera of a user device, transmitting, by the user device, the representation to a treatment planning system, receiving, by the user device, a graphical visualization of a treatment plan for moving the one or more teeth of the user from the treatment planning system, the graphical visualization generated based on the representation, displaying, by the user device, the graphical visualization, wherein the graphical visualization comprises a three-dimensional (3D) representation corresponding to the one or more teeth of the user and an interactive object associated with the treatment plan.