Abstract: A method of anonymizing clinical data may include receiving a three-dimensional model of representation data corresponding to a body part. The representation data may include a clinically relevant region and an anonymization region. The method may also include extracting, from the representation data, clinical representation data corresponding to the clinically relevant region of the representation data and generating artificial representation data corresponding to the anonymization region of the representation data. The method may also include creating, based at least on the clinical representation data and the artificial representation data. The anonymized representation data may substantially preserve the clinically relevant region.

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: A method includes receiving data of a current state of a dental site of a patient, the data comprising a plurality of data items generated from a plurality of oral state capture modalities. The data is processed using a plurality of trained machine learning models that output estimations of one or more oral conditions. The data and/or estimations are processed to generate at least one of a) one or more actionable symptom recommendations for one or more oral health problems associated with the one or more oral conditions or b) one or more diagnoses of the one or more oral health problems. One or more treatment recommendations are generated for treatment of at least one oral health problem based on at least one of the one or more actionable symptom recommendations or the one or more diagnoses.

Abstract: A method includes receiving image data of a current state of a dental site of a patient and processing the image data using a segmentation pipeline to generate an output comprising segmentation information for one or more teeth in the image data and at least one of identifications or locations of one or more oral conditions observed in the image data, wherein each of the one or more oral conditions is associated with a tooth of the one or more teeth. The method includes generating a visual overlay comprising visualizations for each of the one or more oral conditions, outputting the image data to a display, and outputting the visual overlay to the display over the image data.

Abstract: Apparatuses, systems, and methods for importing and/or transferring data, including dental data, from three-dimensional (3D) dental models to two-dimensional (2D) dental images may manipulate the 3D dental model based on a temporomandibular joint. A 2D projected image is generated from a patient's 3D dental model. If the 2D projected image matches (within a threshold) to a patient's 2D dental image (from a photo or the like), then dental data may be transferred. If the 2D projected image does not match, then the 3D dental model may be modified (based on the temporomandibular joint) and a new 2D projected image generated. In this manner, the positions of the 3D dental model may be iterated to generate multiple 2D projected images.

Abstract: A method includes receiving a radiograph of a dental site and processing the radiograph using a segmentation pipeline to identify a plurality of oral conditions for the dental site. Processing the radiograph using the segmentation pipeline comprises: processing the radiograph using one or more first models that perform tooth segmentation, wherein the one or more first models generate a first output of tooth segmentation information comprising identifications and locations of a plurality of teeth in the radiograph; processing the radiograph using one or more second models that generate a second output comprising at least one of identifications or locations of the one or more oral conditions; and performing postprocessing to combine the first output and the second output, wherein as a result of the postprocessing each of the one or more oral conditions is assigned to one or more teeth of the plurality of teeth in the radiograph.

Abstract: A method comprises receiving, by a processing device, clinical data of a first state of one or more dental arches of a patient. The method further comprises determining, by the processing device, one or more stages of an orthodontic treatment plan for correcting the one or more dental arches based on processing of the clinical data. Determining a stage of the orthodontic treatment plan comprises determining one or more actions to be performed with respect to the one or more dental arches and determining a target state of the one or more dental arches that is predicted to result at least in part from the one or more actions.

Abstract: A computing device comprises a memory to store instructions and one or more processors operatively coupled to the memory. Execution of the instructions causes the one or more processors to calculate a quality metric for a region of interest in a received image of a face of a person that comprises a current dentition of the person, wherein data for pixels inside of the region of interest is used to calculate the quality metric, and wherein data for pixels of a second region that is outside of the region of interest is not used to calculate the quality metric; determine whether the quality metric satisfies a criterion; and responsive to determining that the quality metric satisfies the criterion, generate a modified version of the image, wherein the current dentition of the person is replaced with a post-treatment dentition of the person in the modified version of the image.

Abstract: A system comprises a memory and one or more processors operatively connected to the memory. The one or more processors receive a facial image comprising a two-dimensional (2D) depiction of a person's teeth at a first arrangement and receive a tooth model comprising a three-dimensional (3D) depiction of the person's teeth at a second arrangement. The one or more processors obtain color information from the facial image, wherein the color information represents a color scheme of the person's teeth at the first arrangement. The one or more processors generate a photo-realistic 2D depiction of the person's teeth at the second arrangement using the 3D depiction of the person's teeth at the second arrangement and the color information obtained from the facial image. The one or more processors send the photo-realistic 2D depiction of the person's teeth at the second arrangement for display on a display device.

Abstract: Apparatuses and methods for dental image analysis including determining pixel sizes of an image. A pixel size enables conversion of a two-dimension image-based measurement into a 3D measurement. The pixel sizes vary over the image due to differences in distance between the camera taking the image and the object of interest. A three-dimensional model may be registering to the two-dimension image, and the pixel sizes may be identified on a per-object (e.g., per-tooth) basis, per-region basis or at each point in the image. The pixel size scaling may be used to identify features in the image for diagnostic or other dental application purposes.

Abstract: The disclosed systems and methods for anonymizing clinical data may include receiving representation data corresponding to a body part. The representation data may include a clinically relevant region and an anonymization region. The method may include extracting, from the representation data, clinical representation data corresponding to the clinically relevant region of the representation data and generating artificial representation data corresponding to the anonymization region of the representation data. The method may further include creating, based at least on the clinical representation data and the artificial representation data, anonymized representation data that substantially preserves the clinically relevant region. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system includes a memory and a processing device operatively coupled to the memory. The processing device receives an image of a smile of an individual comprising initial contours of teeth of the individual, receives or generates image data comprising target contours of the teeth of an individual, and generates a new image based on the received image data and one or more parametric functions associated with tooth color determined from the image of the smile, wherein a shape of the teeth in the new image is based on the received 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: Embodiments may include: receiving a facial image comprising a 2D depiction of a person's teeth at a first arrangement; receiving a tooth model comprising a 3D depiction of the person's teeth at a second arrangement; obtaining color information from the facial image, wherein the color information represents a color scheme of the person's teeth at the first arrangement; and generating a photo-realistic two-dimensional depiction of the person's teeth at the second arrangement using the 3D depiction of the person's teeth at the second arrangement and the color information obtained from the facial image.

Abstract: The disclosed systems and methods for anonymizing clinical data may include receiving representation data corresponding to a body part. The representation data may include a clinically relevant region and an anonymization region. The method may include extracting, from the representation data, clinical representation data corresponding to the clinically relevant region of the representation data and generating artificial representation data corresponding to the anonymization region of the representation data. The method may further include creating, based at least on the clinical representation data and the artificial representation data, anonymized representation data that substantially preserves the clinically relevant region. Various other methods, systems, and computer-readable media are also disclosed.

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 processing device receives an image and calculates a sharpness metric for a region of interest of the image. 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: 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 sparse voxel representation of the 3D model. Features can be extracted from the sparse voxel representation of the 3D model and input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components.

Abstract: Apparatuses and methods for assessing a dental x-ray image and predicting tooth eruption and/or exfoliation. The apparatuses may use one or more trained neural networks to assess a patient's x-rays as well as dental measurements determined from the patient's x-rays to predict approximately when a permanent tooth may erupt to replace a baby tooth. The neural networks may be trained using a data training set of x-ray images and accompanying dental measurement data.

Abstract: A method includes receiving a selection of an image of a face of an individual from a plurality of images of the face of the individual, determining an orientation of a jaw of the individual in the selected image, and updating an orientation of a three-dimensional (3D) model of a dental arch of the individual to match the orientation of the jaw of the individual in the selected image.

Abstract: A method includes capturing a video comprising a plurality of frames of a face of an individual, determining that the video fails to satisfy one or more quality criteria, and providing guidance of one or more actions to be performed by the individual to cause an updated video to satisfy the one or more quality criteria.