Abstract: Methods and apparatuses for the automatic determination of the quality of a digital three-dimensional (3D) models of a patient's dentition. These methods and apparatuses may determine the if a digital 3D model is of sufficient quality for use in treatment planning and may use a machine learning agent (e.g., neural network) that is trained using a set of defects that are recognized as impacting the quality and/or utility of the digital 3D model for treatment planning of a particular treatment.

Abstract: Methods and apparatuses for automatic treatment planning, including recommendation systems, quality assurance, error prevention, text mining, text matching, and treatment planning optimization.

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 apparatuses for automatic treatment planning, including recommendation systems, quality assurance, error prevention, text mining, text matching, and treatment planning optimization.

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: 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: Methods and apparatuses for adjusting three-dimensional (3D) dental model data of a patient's dentition to detect and remove one or more attachments on a dental structure (e.g., tooth) of the patient's dentition. These methods and apparatuses may be used for generating treatment plans for treating the patient's teeth, including for more accurately and efficiently aligning the patient's teeth.

Abstract: Provided herein are apparatuses (e.g., systems) and methods for assisting in generating and segmenting a 3D dental model of a subject's dentition. A 3D dental model may be generated from a dental scan. The apparatuses described herein can determine if the subject has previously undergone a dental or orthodontic treatment, and the 3D dental model can be compared to prior 3D dental models from the previous treatment(s). In some examples, the 3D dental model can be updated or supplemented with data from the prior 3D dental models.

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: Methods and apparatuses for automatic treatment planning, including recommendation systems, quality assurance, error prevention, text mining, text matching, and treatment planning optimization.

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.