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: An intraoral scanning system comprises an intraoral scanner and a computing device. The computing device receives intraoral scans of a dental site from the intraoral scanner, the intraoral scans comprising at least a first intraoral scan and a second intraoral scan. The computing device determines, for each point in the second intraoral scan based on a first comparison of the first intraoral scan to the second intraoral scan, whether the point is a back-face point or an uncovered point. The computing device determines a region comprising a plurality of points in the second intraoral scan that are uncovered points, determines whether the region satisfies one or more removal criteria, and removes the region from the second intraoral scan responsive to determining that the region satisfies the one or more removal criteria.

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 method of intraoral scanning includes receiving a plurality of intraoral scans of a dental site during an intraoral scanning session, generating a three-dimensional (3D) surface of the dental site from the plurality of intraoral scans, identifying hard tissue and soft tissue in at least one of a) the plurality of intraoral scans of the dental site or b) the 3D surface of the dental site, and displaying a view of the 3D surface, wherein a first visualization is used to display first portions of the 3D surface identified as hard tissue and a second visualization is used to display second portions of the 3D surface identified as soft tissue.

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: Embodiments relate to techniques for real-time and post-scan visualization of intraoral scan data, which may include 3D images, 3D scans, 3D surfaces and/or 3D models. In one embodiment, an intraoral scanning system comprises a plurality of image sensors to periodically generate a set of intraoral two-dimensional (2D) images, wherein for each set of intraoral 2D images each image sensor of the plurality of image sensors is to generate an intraoral 2D image, and wherein relative positions and orientations of the plurality of image sensors are known. The intraoral scanning system further comprises a computing device, wherein the computing device is to perform the following for each set of intraoral 2D images: generate a combined intraoral image based on merging the set of intraoral 2D images together during scanning; and output the combined intraoral image to a display.

Abstract: An intraoral scanning system includes an intraoral scanner and a computing device. The computing device receive a plurality of intraoral scans from the intraoral scanner during an intraoral scanning session; registers the plurality of intraoral scans together based on overlapping features of the plurality of intraoral scans; generates a first three-dimensional (3D) surface based on the plurality of intraoral scans; receives one or more additional intraoral scans; determines that the one or more additional intraoral scans fail to satisfy one or more registration criteria for registering to at least one of the plurality of intraoral scans or the first 3D surface; and generates a second 3D surface based on the one or more additional intraoral scans.

Abstract: An intraoral scanning system comprises an intraoral scanner and a computing device. The computing device receives intraoral scans of a dental site from the intraoral scanner, the intraoral scans comprising at least a first intraoral scan and a second intraoral scan. The computing device determines, for each point in the second intraoral scan based on a first comparison of the first intraoral scan to the second intraoral scan, whether the point is a back-face point or an uncovered point. The computing device determines a region comprising a plurality of points in the second intraoral scan that are uncovered points, determines whether the region satisfies one or more removal criteria, and removes the region from the second intraoral scan responsive to determining that the region satisfies the one or more removal criteria.

Abstract: A method includes determining the following for each point in a second intraoral scan based on a comparison of a first intraoral scan to the second intraoral scan: whether a surface normal at the point is co-directional with a viewing direction associated with the first intraoral scan, wherein points in the second intraoral scan that have a surface normal that is co-directional with the first viewing direction are back-face points; and whether the point is behind a corresponding point in the first intraoral scan, wherein points in the second intraoral scan that are not behind corresponding points in the first intraoral scan are uncovered points. The method further includes determining a region comprising a plurality of points in the second intraoral scan that are uncovered back-face points, determining that the region satisfies one or more removal criteria, and removing the region from the second intraoral.

Abstract: Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned. The scan data may be converted into a 3D model, including a graph-based representation of the 3D model. The graph-based representation can be input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components. Trained machine learning models can also be used to segment graph-based representations of a 3D model of a patient's teeth.

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 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 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 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: In embodiments, a processing device detects a margin line in a model of a preparation from one or more images. The processing device determines, for each segment of a plurality of segments of the margin line, a quality score for the segment. The processing device determines that a segment of the margin line has a quality score that is below a quality threshold, wherein the segment of the margin line was generated based on a first set of images. Responsive to determining that the segment of the margin line has the quality score that is below the quality threshold, the processing devices updates the three-dimensional model of the preparation by replacing a portion of the three-dimensional model associated with the segment of the margin line with image data from at least one of a new image or a second set of images.

Abstract: In embodiments, a processing device generates a three-dimensional model of a dental site from scan data, the three-dimensional model comprising a representation of a tooth, wherein a portion of the three-dimensional model comprises an interfering surface that obscures a portion of the tooth. The processing device receives or generates an image of the tooth, wherein the image depicts the interfering surface. The processing device processes the image to generate a modified image, wherein the portion of the tooth that was obscured by the interfering surface in the image is shown in the modified image. The processing device updates the three-dimensional model of the dental site by replacing, using the modified image, the portion of the three-dimensional model that comprises the interfering surface that obscures the portion of the tooth, wherein the portion of the tooth that was obscured in the three-dimensional model is shown in an updated three-dimensional model.

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.