Abstract: A system includes a memory and a processing device operatively coupled to the memory. The processing device generates one or more parametric functions associated with tooth color based on color data for teeth from a first image, wherein each of the one or more parametric functions comprises a first variable for a first image axis and a second variable for a second image axis. The processing device receives image data comprising new contours of the mouth, wherein one or more of the teeth have a different position in the image data than in the first image. The processing device generates a new image based on the image data and the one or more parametric functions, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the one or more parametric functions to the image data.

Abstract: Provided herein are systems and methods for optimizing a 3D model of an individual's teeth. A 3D dental model may be reconstructed from 3D parameters. A differentiable renderer may be used to derive a 2D rendering of the individual's dentition. 2D image(s) of an individual's dentition may be obtained, and features may be extracted from the 2D image(s). Image loss between the 2D rendering and the 2D image(s) can be derived, and back-propagation from the image loss can be used to calculate gradients of the loss to optimize the 3D parameters. A machine learning model can also be trained to predict a 3D dental model from 2D images of an individual's dentition.

Abstract: A method for dental treatment may include receiving a plurality of images of a patient, the plurality of images including the patient's dentition and an orthodontic appliance while the patient is wearing the orthodontic appliance, determining whether each of the plurality of images satisfy a plurality of detection criteria, segmenting the teeth in the images, segmenting the images to classify each pixel as being of the orthodontic appliance and the teeth, classifying the pixels of the image as being pixels belonging to a space between an aligner and a tooth, assigning the pixels belonging to a space to one or more of the teeth, determining a conversion between image-based spatial measurements to real-world spatial measurements by projecting a tooth from the treatment plan into a plane that corresponds to a plane of a corresponding tooth in the image, and determining a size of each of the one or more spaces.

Abstract: A method for dental treatment may include receiving a plurality of images of a patient's dentition, identifying, from the plurality of images, individual teeth of the patient's dentition, detecting, from the plurality of images, one or more attachments on the patient's dentition, assigning, based on each of the plurality of images, each of the one or more attachments to one of the individual teeth in each image, and combining the assignments of each of the plurality of images for attachment detection results.

Abstract: A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

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 systems and methods for scoring a post-treatment tooth position of a patient's teeth. A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output the post-treatment tooth position of the patient's teeth.

Abstract: Provided herein are systems and methods for scoring a post-treatment tooth position of a patient's teeth. A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output the post-treatment tooth position of the patient's teeth.

Abstract: A system includes a memory and a processing device operatively coupled to the memory. The processing device generates one or more parametric functions associated with tooth color based on color data for teeth from a first image, wherein each of the one or more parametric functions comprises a first variable for a first image axis and a second variable for a second image axis. The processing device receives image data comprising new contours of the mouth, wherein one or more of the teeth have a different position in the image data than in the first image. The processing device generates a new image based on the image data and the one or more parametric functions, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the one or more parametric functions to the image data.

Abstract: Provided herein are methods and apparatuses for analyzing a patient's dental arches in order to generate a treatment plan for the dentition. In particular described herein are methods and apparatuses for determining accurate standardized tooth numbering even when there are missing and/or supernumerary teeth.

Abstract: A method includes determining, from a first image of a mouth, a first region of the first image comprising a representation of teeth; generating one or more parametric functions associated with tooth color based on color data from the first region; receiving image data comprising new contours of the mouth, wherein one or more of the teeth have a different position in the image data than in the first image; determining a second region comprising the teeth in the image data; and generating a new image based on the image data and the one or more parametric functions, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the one or more parametric functions to the second region of the image data.

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: Provided herein are methods and apparatuses for analyzing a patient's dental arches in order to generate a treatment plan for the dentition. In particular described herein are methods and apparatuses for determining accurate standardized tooth numbering even when there are missing and/or supernumerary teeth.

Abstract: A method for dental treatment may include receiving photos capturing a state of a patient's dentition of a patient at a specific time, retrieving a first treatment plan to treat the patient's dentition, identifying an intended arrangement of the first treatment plan at the specific time, evaluating the photo parameters of the photos to generate alignment data to align the intended arrangement of the first treatment plan and the photos, generating an alignment mesh using the alignment data, the alignment mesh comprising a three-dimensional (3D) mesh representation of the patient's dentition at the specific time, performing an evaluation of the first treatment plan for modifications using the alignment mesh, and identifying proposed modifications to the first treatment plan based on the evaluation.

Abstract: A method for image-based orthodontic treatment may include receiving image data of a patient's dentition and an orthodontic appliance, identifying, from the image data, the orthodontic appliance, calculating a misalignment height of a misalignment of the orthodontic appliance with respect to the patient's dentition, determining whether the misalignment height satisfies a misalignment threshold, and in response to satisfying the misalignment threshold, providing a notification.

Abstract: A method of dental treatment may include receiving one or more photo parameters to define clinically acceptable criteria for a plurality of clinically relevant photos of a person's dentition. The clinically acceptable criteria may include at least a plurality of clinically acceptable positions and a plurality of clinically acceptable orientations of teeth relative to a camera. The method may also include gathering a plurality of image-capture rules to capture the plurality of clinically relevant photos. The plurality of image capture rules may be based on the one or more photo parameters. The method may also include providing first automated instructions to capture the plurality of clinically relevant photos of the person's dentition using the plurality of image-capture rules and capturing the plurality of clinically relevant photos with the camera in response to the first automated instructions.

Abstract: A method for dental treatment may include receiving photos of a patient's dentition, gathering treatment parameters for at treatment plan. The treatment parameters may represent attributes of the treatment plan selected by a doctor for the patient's dentition of a patient. The method may include generating, using the photos and the treatment parameters, one or more intelligent guidance rules to guide application of at least a portion of the treatment parameters to the patient's dentition, generating instructions to apply the intelligent patient guidance rules to the patient's dentition, and providing the instructions to the patient to apply the at least a portion of the treatment plan in accordance with the intelligent patient guidance rules.

Abstract: A method for treatment-based photo guidance may include receiving a treatment plan, determining the movement of a first one or more teeth based on the movement of tooth models in the treatment plan, and determining one or more photo views for capturing the movement of the first one or more teeth.

Abstract: A method of dental treatment may include receiving photos of a person's dentition, identifying a stage of a treatment plan administered to the person's dentition, gathering a three-dimensional (3D) model of the person's dentition corresponding to the stage of the treatment plan, projecting attributes of the 3D model of the person's dentition onto an image plane to get a projected representation of the person's dentition at the stage of the treatment plan, comparing the photos to the projected representation to derive an error image representing the comparison, and analyzing the error image for discrepancies, wherein the discrepancies represent one or more deviations of the person's dentition from the stage of the treatment plan.

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.