Abstract: Systems and methods for identifying teeth in a patient image are provided. A system can include processor(s) and a memory operably coupled to the processor(s) and storing instructions that, when executed by the processor(s), cause the system to perform operations including accessing a 2D image including a depiction of a patient's teeth, applying a segmentation algorithm to the 2D image, and generating tooth assignments. The segmentation algorithm may be configured to identify a plurality of regions in the 2D image, each region corresponding to an estimated location of a tooth of the patient's teeth in the 2D image, and determine a tooth mask and a tooth identifier for each tooth, where the determination for each tooth is based on features of the region corresponding to the estimated location of the tooth and features of the regions corresponding to estimated locations of one or more teeth proximate to the tooth.

Abstract: A system may include one or more processors and memory comprising instructions that when executed by the one or more processors cause the system to receive a first dental image depicting a patient's teeth in a first view and crop the first dental image. The system may generate a score of overall image quality for the cropped first dental image using a trained neural network. The trained neural network may be trained using a plurality of ranked sub-sets of dental images and a loss function may be configured to preserve a relative ranking order of the ranked sub-sets of dental images. The sub-sets of dental images may be ranked based on perceived overall image quality. The system may determine that the score is below a threshold and request, based on the determination, a second dental image depicting the patient's teeth in the first view.

Abstract: The present technology provides solutions for anonymizing patient data and in particular, for anonymizing potentially unique patient-identifying information while retaining clinically relevant information, for example, that can be used to illustrate the results or outcomes of a clinical intervention. In some aspects, a process of the disclosed technology can include steps for receiving patient data, wherein the patient data comprises a patient image corresponding with a patient, receiving treatment data, wherein the treatment data is based on a treatment goal for the patient, automatically determining, using one or more semantic controls, one or more anonymization parameters for the patient image; and generating, based on the one or more anonymization parameters, an anonymized patient image. Systems and machine-readable media are also provided.

Abstract: A method includes receiving a video comprising a face of an individual, the video comprising a current condition of a dental site of the individual. The method includes determining or receiving an altered condition of the dental site and modifying the video by replacing the current condition of the dental site with the altered condition of the dental site in the video.

Abstract: Methods and systems for processing dental workflows. In a pre-routing module, a determination may be made as to whether process(es) in generating an output workflow performed on a digital dental model should be manually or automatically based on comparing patient-specific clinical parameters against clinical rules. In a post-routing module, a determination may be made as to whether the output workflow contains output parameter value(s) that is outside of a range of acceptable output parameter values. An indicator to manually perform the process(es) may be transmitted if the post-routing module determines that output parameters value(s) is outside of the range of acceptable output parameter values. The output workflow may be modified based on the process(es) being manually or semi- manually performed. Dental appliance(s) may be fabricated in accordance with the modified output workflow.

Abstract: Systems and methods for determining camera pose for a patient image are provided. A system can include one or more processors and a memory operably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including receiving a 2D image including a depiction of teeth of at least one jaw of a patient, identifying a set of tooth landmarks representing geometries and locations of the teeth in the 2D image, where the set of tooth landmarks may be identified by inputting the 2D image into a first machine learning model, and determining a camera pose representing an estimated spatial relationship between the imaging device and the at least one jaw, where the camera pose is determined by inputting the identified set of tooth landmarks into a second machine learning model.

Abstract: A method is provided for generating and/or displaying a multimodal timelapse visualization. In some cases, the method can include identifying a plurality of records associated with a dental arch of a patient. Each record corresponds to a point in time and to an imaging modality of a plurality of imaging modalities. Based on processing of at least one record, a clinical finding is determined for the patient. A primary imaging modality is determined based at least in part on the clinical finding. At least a subset of the plurality of records is normalized. A timelapse visualization representing the normalized subset of records displayed in chronological order of the plurality of points in time is generated. The timelapse visualization is provided to a user device for presentation in a user interface of the user device.

Abstract: Methods and systems for simulating dental whitening. A three-dimensional (3D) model of a dentition may be sent to a treatment planning system. A whitened 3D model of the dentition may be received from the treatment planning system. The whitened 3D model may be generated by: converting color values of the 3D model from a first color space to a second color space, wherein the second color space includes one or more color components and one or more lightness components; and generating the whitened 3D model by adjusting a lightness value of one or more portions of the 3D model. The whitened 3D model may be displayed.

Abstract: A method is provided for generating and/or displaying a dynamic tooth chart. In some cases, the method can include receiving dataset(s) associated with a patient. Each dataset comprises data corresponding to an imaging modality of a plurality of imaging modalities and provides information on a dentition of the patient. The datasets are processed to generate, for each dataset, one or more tooth portions. Each tooth portion comprises an image of one or more teeth of the patient. The one or more tooth portions are arranged into a dental chart. A visualization of the dental chart of the patient is generated and provided to a user device for presentation in a user interface. In some cases, a method can include determining one or more chart annotations can by performing a shape analysis, a teeth arrangement analysis, and/or a material analysis on 3D dental model(s) and/or 2D images of the patient's jaw.

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.