Abstract: A system includes an intraoral scanner and a computing device. The intraoral scanner generates an intraoral scan of a dental site. The computing device processes an input comprising data from the intraoral scan using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model generates an output comprising, for each point in the intraoral scan, an indication as to whether the point belongs to a first dental class that represents excess material. The computing device determines, based on the output, one or more points in the intraoral scan that are classified as excess material. The computing device then hides or removes, from at least one of the intraoral scan or a virtual three-dimensional (3D) model generated using the intraoral scan, data for the one or more points that are classified as excess material.

Abstract: Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.

Abstract: An apparatus (system, device, method, and the like) is disclosed for refining a three-dimensional (3D) model, particularly 3D models of a subject's dentition. An initial 3D model is received or generated along with a plurality of two-dimensional (2D) images corresponding to the 3D model. The 3D model is refined using edge boundaries of a space around or between two or more objects of the 3D model identified from the 2D images.

Abstract: Provided herein are devices and methods generating a panoramic rendering of a subject’s teeth. Methods and processes are provided to image the subject’s teeth with a dental scan. Methods and processes are also provided to automatically 3D render the subject’s teeth with the scan images. Methods and apparatuses are also provided to generate simulated panoramic views of the subject’s dentition from various perspectives.

Abstract: Provided herein are devices and methods generating a panoramic rendering of a subject's teeth. Methods and processes are provided to image the subject's teeth with a dental scan. Methods and processes are also provided to automatically 3D render the subject's teeth with the scan images. Methods and apparatuses are also provided to generate simulated panoramic views of the subject's dentition from various perspectives.

Abstract: Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.

Abstract: A system includes an intraoral scanner and a computing device. The intraoral scanner generates an intraoral scan of a dental site. The computing device processes an input comprising data from the intraoral scan using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model generates an output comprising, for each point in the intraoral scan, an indication as to whether the point belongs to a first dental class that represents excess material. The computing device determines, based on the output, one or more points in the intraoral scan that are classified as excess material. The computing device then hides or removes, from at least one of the intraoral scan or a virtual three-dimensional (3D) model generated using the intraoral scan, data for the one or more points that are classified as excess material.

Abstract: A method includes processing an input comprising data from an intraoral image using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the intraoral image, a first probability that the pixel belongs to a first dental class and a second probability that the pixel belongs to a second dental class, wherein the first dental class represents excess material, the excess material comprising material other than teeth or gums. The method further includes determining, based on the probability map, one or more pixels in the intraoral image that are classified as excess material. The method further includes hiding or removing from the intraoral image data for the one or more pixels that are classified as excess material.

Abstract: Methods and apparatuses (including systems and devices) for modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc. In some implementations one or more automated machine learning agents may modify one or more subsets of 3D models of the subject's oral cavity using height map data to identify, segment and/or modify to mesh regions of a 3D model constructed from a plurality of 2D images of the subject's dental cavity.

Abstract: A processing device receives a plurality of intraoral scans of a dental arch. The processing device determines, from the plurality of intraoral scans, a first set of intraoral scans that depict a restorative object and a second set of intraoral scans that fail to depict any restorative object. The processing device generates a multi-resolution three-dimensional model of the dental arch, wherein a first portion of the multi-resolution three-dimensional model is generated from the first set of intraoral scans, has a first resolution and represents the restorative object, and wherein a second portion of the multi-resolution three-dimensional model is generated from the second set of intraoral scans, has a second resolution and represents a remainder of the dental arch, wherein the first resolution is greater than the second resolution.

Abstract: A processing device may receive a plurality of intraoral scans of the patient that were generated by an intraoral scanner, determine a three-dimensional surface of at least a portion of one or more dental arch of the patient using the plurality of intraoral scans, automatically determine whether a restorative dental object is represented in at least one of the three-dimensional surface or one or more intraoral scans of the plurality of intraoral scans, and automatically generate a prescription for treating the one or more teeth based at least in part on at least one of a) a presence or absence of a restorative dental object in at least one of the three-dimensional surface or the one or more intraoral scans or b) a location of the restorative dental object in the one or more dental arch of the patient.

Abstract: A method of generating a 3D model includes: determining a 3D surface comprising a preparation tooth using a first plurality of intraoral scans generated by an intraoral scanner at a first time; receiving one or more additional intraoral scans of the preparation tooth that were generated by the intraoral scanner at a second time; determining a change to one or more portions of the preparation tooth between the three-dimensional surface and the one or more additional intraoral scans; determining, based at least in part on the change to the preparation tooth, whether to use a) the 3D surface, b) data from the one or more additional intraoral scans or c) a combination of the 3D surface and the data from the one or more additional intraoral scans to depict one or more portions of the preparation tooth; and generating the 3D model comprising the preparation tooth.

Abstract: A method of intraoral scanning includes receiving a first one or more intraoral scans of a patient's oral cavity; automatically determining, based on processing of the first one or more intraoral scans, a first scanning role associated with the first one or more intraoral scans, wherein the first scanning role is a first one of an upper dental arch role, a lower dental arch role or a bite role; and determining a first three-dimensional surface associated with the first scanning role.

Abstract: Provided herein are systems and methods for monitoring a subject's teeth during orthodontic treatment. A wide angle (e.g., fisheye) image showing an occlusal view of both arches of the subject's dentition and may be input into a monitoring system configured compare the image to the treatment plan to determine how closely the current position of the subject's teeth tracks with the expected or desired positions according to the orthodontic treatment plan.

Abstract: Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.

Abstract: Provided herein are devices and methods generating a panoramic rendering of a subject's teeth. Methods and processes are provided to image the subject's teeth with a dental scan. Methods and processes are also provided to automatically 3D render the subject's teeth with the scan images. Methods and apparatuses are also provided to generate simulated panoramic views of the subject's dentition from various perspectives.

Abstract: Methods and apparatuses (including systems and devices) for modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc. In some implementations one or more automated machine learning agents may modify one or more subsets of 3D models of the subject's oral cavity using height map data to identify, segment and/or modify to mesh regions of a 3D model constructed from a plurality of 2D images of the subject's dental cavity.

Abstract: A method includes processing an input comprising data from an intraoral image using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the intraoral image, a first probability that the pixel belongs to a first dental class and a second probability that the pixel belongs to a second dental class, wherein the first dental class represents excess material, the excess material comprising material other than teeth or gums. The method further includes determining, based on the probability map, one or more pixels in the intraoral image that are classified as excess material. The method further includes hiding or removing from the intraoral image data for the one or more pixels that are classified as excess material.