Abstract: Various apparatuses are disclosed (e.g., system, device, method, or the like) for guiding the capture and assessing the quality of an image, including dental images. The apparatuses may use trained neural networks to examine images and provide users feedback regarding image quality. The neural networks may be trained based on images within image groups that have been ranked based on perceived image quality.

Abstract: Systems and methods for identifying teeth in a patient image are provided. For example, a computer-implemented method can include, by one or more processors, accessing a 2D image including a depiction of a patient's teeth, where a plurality of the depicted patient's teeth are annotated with tooth identifiers according to a first tooth identification scheme. The computer-implemented method can further include transmitting, to a server computing device, the 2D image, and receiving from the server computing device, a second tooth identification scheme for the patient's teeth in the 2D image. The second tooth identification scheme can be generated by accessing a 3D model of the patient's teeth, projecting the 3D model onto the 2D image, comparing the projection to the 2D image to determine a probability parameter for each of one or more teeth in the 2D image, and determining, based on the probability parameters, the second tooth identification scheme.

Abstract: Methods and systems for simulating dental whitening. A two-dimensional (2D) image of a patient's oral cavity may be converted from a first color space to a CIELAB color space. A change in a blue-yellow color value (delta b value) with which to whiten the patient's teeth may be selected to increase a blue hue and decrease a yellow hue in the 2D image. A threshold lightness value of individual teeth in the 2D image may be identified. The 2D image in the CIELAB color space may be transformed by adjusting delta b values of each tooth based, at least in part, on an evaluation of a lightness value of each tooth relative to the threshold lightness value to form a whitened 2D image. Adjusting the delta b values may include limiting the delta b values to within a predetermined range. A whitened 2D image may be outputted.

Abstract: A method includes receiving, by a user device, a prompt associated with treatment of a patient. The method includes determining a treatment context for the treatment of the patient based on access to a back-end treatment context support system. The method includes processing the prompt in view of the treatment context using one or more trained machine learning models to generate one or more actionable recommendations. The method includes outputting the one or more actionable recommendations via at least one of the user device or an additional device associated with the user device.

Abstract: In embodiments, a system comprises a server computing device comprising a messaging platform configured to provide messaging between a plurality of different types of medical applications, a first computing device of a doctor and a second computing device of a patient or prospective patient. The first computing device comprises a doctor-focused medical application and a first chat module that integrates with the doctor-focused medical application and that enables the doctor-focused medical application to interface with the messaging platform. The second computing device comprises a patient-focused medical application and a second chat module that integrates with the patient-focused medical application and that enables the patient-focused medical application to interface with the messaging platform, wherein the patient-focused medical application has different functionality than the doctor-focused medical application.

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: Systems and methods for evaluating dental appliance fit are provided, including: accessing a 2D image including a depiction of a dental appliance being worn on a patient's teeth; accessing a 3D digital representation of the patient's teeth; identifying a line associated with a tooth in the 3D digital representation; projecting the line onto the tooth in the 2D image; determining a distance between an edge of the tooth in the 2D image and an edge of the dental appliance in the 2D image, based on the projected line; and outputting an indication of a fit parameter for display on a display device, where the fit parameter is based on the determined distance.

Abstract: A system for treatment-based photo guidance for acquiring images of a patient's teeth may include one or more processors and memory. The memory may include instructions that when executed, cause the system to access patient-specific anatomical data associated with a dentition of the patient, access a treatment plan for moving teeth of a patient, and determine, based on the patient-specific anatomical data and the treatment plan, one or more photo views for capturing one or more teeth of the patient.

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: 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 method is provided for assessing and categorizing temporomandibular disorder (TMD). In some cases, the method can include receiving data representing a potential for TMD of a patient. The method can include processing the data to identify an indicator of the TMD. The method can include identifying a treatment recommendation based on the indicator of the TMD. The method can include providing the treatment recommendation for display on a user device.

Abstract: A method is provided for measuring and categorizing gingival recession. In some cases, the method can include receiving intraoral scan data of a dentition of a patient. The method can include segmenting the intraoral scan data into a plurality of oral structures that comprise at least a tooth in the dentition of the patient, a gingiva, and a representation of an intersection between a first portion of the tooth and a second portion of the tooth. The method can include determining a gingival recession measurement indicative of a distance between the gingiva and the intersection. The method can include providing, to the user device, the gingival recession measurement.

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: Systems and methods for scoring a post-treatment tooth position of a patient's teeth. Raw features of the patient's teeth may be determined from a 3D model of the patient's teeth. Engineered features may be created based on the raw features, which may be applied to a classifier trained to provide a post-treatment tooth position score for the patient's teeth. The post-treatment tooth position score may include a label for each tooth and an associated tooth position score and may be generated as output.

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: 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: Systems, apparatuses, and methods disclosed herein are directed to systems, methods, and apparatuses for enabling a patient to capture intra-oral images and for the processing of those images to assist a dentist or orthodontist in evaluating the state of a patient's teeth and the development or progress of a treatment plan.

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: 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: 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.