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: An intraoral scanner generates 2D images of a dental site and 3D intraoral scans of the dental site. The computing device receives the 2D images of the dental site and the 3D intraoral scans of the dental site from the intraoral scanner, generates a 3D model of the dental site based on the 3D intraoral scans of the dental site, and processes at least one of a) one or more of the 2D images of the dental site, b) one or more of the 3D intraoral scans of the dental site, or c) data from the 3D model of the dental site to identify one or more intraoral areas of interest (AOIs) at the dental site. The computing device determines a dental condition associated with the one or more intraoral AOIs, and determines a manner for scanning the one or more intraoral AOIs.

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 for distinguishing between types of intraoral areas of interest (AOIs) includes receiving or generating a first 3D model comprising teeth; receiving or generating a second 3D model comprising the teeth; comparing the first 3D model to the second 3D model; determining a difference between the first 3D model and the second 3D model at a dental site comprising a tooth; determining an intraoral AOI that is associated with the difference; determining whether the difference comprises a first type of change indicative of a first type of intraoral AOI or a second type of change indicative of a second type of intraoral AOI; and responsive to determining that the difference comprises the first type of change, determining that the intraoral AOI is the first type of intraoral AOI and generating an indicator of the first type of intraoral AOI for the tooth.

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: An orthodontic positioning device and methods for making an orthodontic positioning device including a first patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in one of an upper jaw and a lower jaw. The first appliance includes a hook configured to receive an orthodontic elastic band. The orthodontic positioning device also includes a second patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in the other of the upper jaw and the lower jaw. The second appliance includes a cutout operable to expose an orthodontic elastic band receiving member.

Abstract: An orthodontic positioning device including a patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in one of an upper jaw and a lower jaw and comprising a cutout operable to expose an orthodontic elastic band receiving member.

Abstract: In an implementation, a processing device receives first intraoral scan data generated at a first time and analyzes the first intraoral scan data to determine a first representation of a caries from the first intraoral scan data. The processing device receives second intraoral scan data generated at a second time and analyzes the second intraoral scan data to determine a second representation of the caries from the second intraoral scan data. The processing device compares the second representation of the caries to the first representation of the caries and tracks the caries over time based on a difference between the second representation of the caries and the first representation of the caries.

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: A method for distinguishing between types of intraoral areas of interest (AOIs) includes receiving or generating a first 3D model comprising teeth; receiving or generating a second 3D model comprising the teeth; comparing the first 3D model to the second 3D model; determining a difference between the first 3D model and the second 3D model at a dental site comprising a tooth; determining an intraoral AOI that is associated with the difference; determining whether the difference comprises a first type of change indicative of a first type of intraoral AOI or a second type of change indicative of a second type of intraoral AOI; and responsive to determining that the difference comprises the first type of change, determining that the intraoral AOI is the first type of intraoral AOI and generating an indicator of the first type of intraoral AOI for the tooth.

Abstract: In embodiments, a first 3D model of a patient's teeth based on first intraoral scan data taken at a first time is processed to recognize the teeth in the first 3D model. A second 3D model of the patient's teeth based on second intraoral scan data taken at a second time is processed to recognize the teeth in the second 3D model. The first 3D model is compared to the second 3D model and a plurality of AOIs in the second virtual 3D model representative of at least one of tooth wear, tooth breakage, tooth movement, gingival recession or gingival swelling are determined based on the comparison. The teeth recognized in the first 3D model are compared to the teeth recognized in the second 3D model, and one or more AOIs in the second virtual 3D model representative of tooth wear or tooth breakage are determined based on the comparison.

Abstract: In an implementation, a processing device receives first intraoral scan data generated at a first time and analyzes the first intraoral scan data to determine a first representation of a caries from the first intraoral scan data. The processing device receives second intraoral scan data generated at a second time and analyzes the second intraoral scan data to determine a second representation of the caries from the second intraoral scan data. The processing device compares the second representation of the caries to the first representation of the caries and tracks the caries over time based on a difference between the second representation of the caries and the first representation of the caries.

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: 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: In embodiments, a first 3D model of a patient's teeth based on first intraoral scan data taken at a first time is processed to recognize the teeth in the first 3D model. A second 3D model of the patient's teeth based on second intraoral scan data taken at a second time is processed to recognize the teeth in the second 3D model. The first 3D model is compared to the second 3D model and a plurality of AOIs in the second virtual 3D model representative of at least one of tooth wear, tooth breakage, tooth movement, gingival recession or gingival swelling are determined based on the comparison. The teeth recognized in the first 3D model are compared to the teeth recognized in the second 3D model, and one or more AOIs in the second virtual 3D model representative of tooth wear or tooth breakage are determined based on the comparison.

Abstract: A processing device performs image registration between a plurality of intraoral images of a dental site. The processing device identifies a candidate intraoral area of interest from a first intraoral image of the plurality of intraoral images. The processing device verifies the candidate intraoral area of interest as an intraoral area of interest based on a comparison of a second intraoral image to the first intraoral image. The processing device displays a view of the dental site where the intraoral area of interest is hidden and an indication of the hidden intraoral area of interest is visible.

Abstract: A processing device receives a first virtual 3D model and a second virtual 3D model, each comprising one or more teeth of a patient. The processing device compares the first virtual 3D model to the second virtual 3D model and determines, as a result of the comparing, a difference between the first virtual 3D model and the second virtual 3D model at a dental site comprising a tooth of the one or more teeth. The processing device determines whether the difference comprises a first type of change to the tooth that is indicative of tooth wear or a second type of change to the tooth that is indicative of tooth movement associated with orthodontic treatment. Responsive to determining that the first difference comprises the first type of change to the first tooth, the processing device generates a first indicator of the tooth wear for the tooth.

Abstract: An orthodontic positioning device and methods for making an orthodontic positioning device including a first patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in one of an upper jaw and a lower jaw. The first appliance includes a hook configured to receive an orthodontic elastic band. The orthodontic positioning device also includes a second patient removable orthodontic tooth positioning appliance having teeth receiving cavities shaped to receive and apply a resilient positioning force to a patient's teeth provided in the other of the upper jaw and the lower jaw. The second appliance includes a cutout operable to expose an orthodontic elastic band receiving member.