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: 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: 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 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: In a technique to assess the blurriness of an image, an image of a face is received, the image including a depiction of lips. A processing device determines a region of interest in the image, wherein the region of interest comprises an area inside of the lips. The processing device applies a focus operator to the pixels within the region of interest, and calculates a sharpness metric for the region of interest using an output of the focus operator. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

Abstract: Embodiments include receiving image data comprising contours of teeth of an individual and generating a new image based on the image data and one or more parametric functions associated with tooth color that comprise a first variable for a first image axis and a second variable for a second image axis, wherein a shape of the teeth in the new image is based on the image data and a color of the teeth in the new image is based on applying the one or more parametric functions to at least a portion of the image data.

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: In a technique to assess the blurriness of an image, an image of a face is received, the image including a depiction of lips. A processing device determines a region of interest in the image, wherein the region of interest comprises an area inside of the lips. The processing device applies a focus operator to the pixels within the region of interest, and calculates a sharpness metric for the region of interest using an output of the focus operator. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

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 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 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: 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 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 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: 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: 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: 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.