Abstract: A method and/or system generates a video of teeth of an individual over time. In one example, images comprising teeth of an individual are received, wherein the images are arranged in a sequence and each image is associated with a different stage of treatment of the teeth. One or more of the images are modified and/or replaced to align the images to one another. One or more synthetic images are generated, wherein each synthetic image is generated based on a pair of sequential images in the sequence and is an intermediate image that comprises an intermediate state of the teeth between a first state of a first image of the pair of sequential images and a second state of a second image of the pair of sequential images. A video is then generated that comprises the images and the one or more synthetic images.

Abstract: The present disclosure is directed to capturing a 3-dimensional representation of a patient's face from multiple angles to be integrated with (fused) with at least a 3-dimensional intra-oral mesh of the patient's teeth for purposes of visualizing a dental treatment plan. In one aspect, a method includes capturing media of a patient's face from multiple angles, using at least one device; transforming the media into a 3-dimensional representation of the patient's face; and transmitting, the 3-dimensional representation of the patient's face to one or more processing components to be integrated with a 3-dimensional representation of the patient's intra-oral scan to visualize a dental treatment plan for the patient.

Abstract: Embodiments include obtaining an input image depicting a body part of a person and processing the input image against a set of semantic landmarks representing landmarks of the body part; obtaining a mesh model for a set of images; generating, from the mesh model and the set of semantic landmarks, a body part mesh of the person, wherein the body part mesh is an approximation of a 3D model for the body part depicted in the input image; obtaining a target body part mesh data structure, distinct from the body part mesh; and generating a modified view image of the body part, modified to reflect differences between the target body part mesh data structure and the body part mesh while retaining at least some texture of the body part from the input image.

Abstract: A method may include obtaining a first 3D model of an upper jaw of a patient using an intraoral scanner and obtaining a second 3D model of the lower jaw of the patient using the intraoral scanner. The method may also include capturing a series of 2D images of the upper and lower jaws of the patient as the patient is moves the upper jaw and lower jaw in dynamic occlusion and processing the captured series of 2D images to identify features associated with the upper jaw of the patient and the lower jaw of the patient. For each 2D image in the captured series of 2D images, the method may include identifying a relative position of the first 3D model and the second 3D model based on alignment of features in the first 3D model and second 3D model with the features identified in the 2D image to generate a series of relative positions of the first 3D model and the second 3D model.

Abstract: A medical image might be generated for a patient image using a convolutional neural network trained on prior patient pre-procedure and post-procedure 2D images. A method might generate 3D models from pre-procedure 2D images and from post-procedure 2 images, and train the convolutional neural network with training images being the 3D models, to generate at least one 3D model of the present patient from the 2D image of the present patient, apply the 3D model of the present patient to the convolutional neural network in an inference stage, and apply patient-specific parameters derived from the proposed surgical procedure as a second input to the convolutional neural network in the inference stage to generate an inferred post-surgery 3D model of the present patient given the patient-specific parameters.

Abstract: The present technology provides solutions for anonymizing patient data and in particular, for anonymizing potentially unique patient-identifying information while retaining clinically relevant information, for example, that can be used to illustrate the results or outcomes of a clinical intervention. In some aspects, a process of the disclosed technology can include steps for receiving patient data, wherein the patient data comprises a patient image corresponding with a patient, receiving treatment data, wherein the treatment data is based on a treatment goal for the patient, automatically determining, using one or more semantic controls, one or more anonymization parameters for the patient image; and generating, based on the one or more anonymization parameters, an anonymized patient image. Systems and machine-readable media are also provided.

Abstract: A method includes receiving a) orthognathic patient data comprising information representing an initial state of a patient and b) plurality of treatment parameters that are based on a treatment goal for the patient. The method further includes generating an output state of the patient based on the initial state of the patient and the plurality of treatment parameters, calculating an objective function based on the output state of the patient and the treatment goal of the patient, and updating one or more of the treatment parameters based on the objective function.

Abstract: Aspects of the disclosed technology provide solutions of simulating patient treatment outcomes and in particular, for estimating treatment parameters that can be optimally modified to achieve a desired treatment outcome. In some aspects, a process of the disclosed technology includes steps for receiving patient data, receiving a plurality of treatment parameters, and generating an output state of the patient based on the initial state of the patient and the treatment goal for the patient. In some aspects, the process can further include steps for calculating an objective function based on the output state of the patient and the treatment goal of the patient, and updating one or more of the treatment parameters based on the objective function. Systems and machine-readable media are also provided.

Abstract: A medical image might be generated for a patient image using a convolutional neural network trained on prior patient pre-procedure and post-procedure 2D images. A method might generate 3D models from pre-procedure 2D images and from post-procedure 2 images, and train the convolutional neural network with training images being the 3D models, to generate at least one 3D model of the present patient from the 2D image of the present patient, apply the 3D model of the present patient to the convolutional neural network in an inference stage, and apply patient-specific parameters derived from the proposed surgical procedure as a second input to the convolutional neural network in the inference stage to generate an inferred post-surgery 3D model of the present patient given the patient-specific parameters.