Align Technology, Inc.

Dmitry Guskov

Sergey Grebenkin

Alexander Vovchenko

Roman A Roschin

Evgenii Vladimirovich Karnygin

Alexander Beliaev

Alexey Vladykin

Roman Gudchenko

Vasily Paraketsov

Mikhail Gorodilov

Denis Durdin

Roman Solovyev

Dmitrii Ischeykin

Ivan Potapenko

Elizaveta Ulianenko

Nikolay Zhirnov

Anzhelika Son

Leonid Trofimov

Alexey Lazarev

Ivan Slepynin

Christopher Eric Cramer

Aleksandr Sergeevich Karsakov

Aleksandr Anikin

Mikhail Toporkov

Anton Baskanov

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. During dentition segmentation, a determination can be made whether trimming portions of the 3D model is desirable. A recommendation can be made for 3D model trimming based on the determination.

Molar trimming prediction and validation using machine learning

Methods for detecting missing or ectopic teeth. At least one tooth that is missing or ectopic may be detected in a dental arch by determining that a gap between adjacent teeth of a digital model of the dental arch exceeds a gap threshold. At least one tooth of the dental arch may be determined to be ectopic by performing an ectopic analysis of a region of the digital model of the dental arch above or below the gap. Anatomical identifiers may be assigned to the teeth of the digital model of the dental arch to account for the at least one tooth that is missing or ectopic. An orthodontic treatment plan to reposition one or more teeth of the dental arch using the assigned anatomical identifiers of the teeth of the digital model of the dental arch may be created.

Detection of missing or ectopic teeth

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.

Segmentation quality assessment

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.

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.

Machine learning dental segmentation methods using sparse voxel representations

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.

Automatic ectopic teeth detection on scan

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

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.

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Moscow, Russia

Dmitry Guskov