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: Methods and apparatuses for generating hybrid (e.g. fused) cone-beam computed tomography (CBCT) scan with an intraoral scan data. Also described herein are methods and apparatuses for more efficiently processing CBCT data. Also described herein are methods and apparatuses for using the hybrid CBCT and intraoral scan data to enhance dental and/or orthodontic treatment planning including interactively presenting the hybrid CBCT and intraoral scans and engaging with a user to generate one or more treatment plans and/or dental appliances for treating a patient according to a treatment plan that take into account the hybrid CBCT and intraoral scan.

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

Abstract: Systems and methods including: receiving, from a digital imaging device, one or more 2D images of a patient's face including a mouth region, where the one or more 2D images include a depiction of the patient's teeth in an initial arrangement; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient's teeth based on the one or more received 2D images; generating a realistic composite 2D image depicting the patient's face with the 2D rendering depicting the post-treatment arrangement of the patient's teeth, where the generating includes positioning the 2D rendering within a mouth opening of the patient's face in the realistic composite 2D image; and sending the realistic composite 2D image to an output device for display on a user interface of the output device.

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: Methods and systems including: receiving one or more 2D images of a patient's face including a depiction of the patient's teeth in an initial arrangement; extracting teeth contours of the patient's teeth from within the mouth region of the one or more received 2D images of the patient's face; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient's teeth based on the one or more received 2D images; generating a realistic composite 2D image depicting the patient's face with the 2D rendering depicting the post-treatment arrangement of the patient's teeth; and sending the realistic composite 2D image for display on a user interface of an output device.

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: Systems and methods for generating a virtual depiction of an orthodontic treatment of a patient are provided. In some embodiments, a system includes one or more processors, and a memory operably coupled to the one or more processors. The memory can include instructions that, when executed by the one or more processors, cause the system to perform operations including: receiving one or more 2D images of a patient’s face including a mouth region; extracting teeth contours of the patient’s teeth from within the mouth region of the received 2D images; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient’s teeth; generating a realistic composite 2D image depicting the patient’s face with the 2D rendering depicting the post-treatment arrangement of the patient’s teeth; and sending the realistic composite 2D image to an output device for display on a user interface of the output device.

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: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

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: The present disclosure provide for methods of planning restorative procedures. Methods may involve identifying an initial position of a patient's teeth based on a digital representation of the patient's teeth, and determining one or more candidate orthodontic positions for the patient's teeth, wherein each of the one or more candidate orthodontic positions is a target position of the patient's teeth being considered. Methods may further involve virtually modeling a first restorative object at a first evaluated restorative object position on one of the patient's teeth, evaluating a first amount of tooth mass removal corresponding to the first restorative object based on the first evaluated restorative object position and one of the candidate orthodontic positions, and determining a final orthodontic position and a final restorative object position based on the first amount of tooth mass removal.

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.