Abstract: Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.

Abstract: A method for determining tooth numbers of teeth within a dental arch includes receiving a digital scan of a patient's dentition, detecting a cusp associated with at least one tooth in the digital scan, where the cusp is detected based at least in part on a radius of a surface of the cusp, matching a molar shape of the at least one tooth, and identifying the patient's molar tooth based on the molar shape.

Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). 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 probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.

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: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Features of the patient's teeth can be determined, including a tooth center point of at least three of the patient's teeth. The features of the patient's teeth are used to access a parametric model. A trim plane normal vector is identified for a distal tooth. Trimming of the 3D tooth model is based on the trim plane normal vector.

Abstract: Methods for automatically segmenting a 3D model of a patient's teeth may include scanning a patient's dentition and converting the scan data 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: Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.

Abstract: Provided herein are orthodontic devices and methods for patients with missing or ectopic teeth. Methods and processes are provided to properly number the teeth of a patient's arch after a dental scan. Methods and processes are also provided to automatically detect missing or ectopic teeth after a dental scan. Methods of designing and manufacturing the aligner are also provided.

Abstract: Provided herein are orthodontic devices and methods for patients with missing or ectopic teeth. Methods and processes are provided to properly number the teeth of a patient's arch after a dental scan. Methods and processes are also provided to automatically detect missing or ectopic teeth after a dental scan. Methods of designing and manufacturing the aligner are also provided.

Abstract: Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.

Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). 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 probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.

Abstract: Provided herein are orthodontic devices and methods for patients with missing or ectopic teeth. Methods and processes are provided to properly number the teeth of a patient's arch after a dental scan. Methods and processes are also provided to automatically detect missing or ectopic teeth after a dental scan. Methods of designing and manufacturing the aligner are also provided.

Abstract: Provided herein are orthodontic devices and methods for patients with missing or ectopic teeth. Methods and processes are provided to properly number the teeth of a patient's arch after a dental scan. Methods and processes are also provided to automatically detect missing or ectopic teeth after a dental scan. Methods of designing and manufacturing the aligner are also provided.

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: Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.

Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). 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 probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.

Abstract: Provided herein are orthodontic devices and methods for patients with missing or ectopic teeth. Methods and processes are provided to properly number the teeth of a patient's arch after a dental scan. Methods and processes are also provided to automatically detect missing or ectopic teeth after a dental scan. Methods of designing and manufacturing the aligner are also provided.