Align Technology, Inc.

Vasily Paraketsov

Roman A Roschin

Evgenii Vladimirovich Karnygin

Sergey Grebenkin

Roman Gudchenko

Alexander Vovchenko

Dmitry Guskov

Denis Durdin

Roman Solovyev

Dmitrii Ischeykin

Ivan Potapenko

Mikhail Gorodilov

Alexander Beliaev

Alexey Vladykin

Elizaveta Ulianenko

Nikolay Zhirnov

Anzhelika Son

Leonid Trofimov

Grigoriy Yazykov

Christopher Eric Cramer

Aleksandr Sergeevich Karsakov

Aleksandr Anikin

Mikhail Toporkov

Ruslan Matvienko

Andrey Romanov

Anton Baskanov

Ilfat Sabirov

Dmitry Kirsanov

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

Automatic application of doctor's preferences workflow using statistical preference analysis

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.

Automatic segmentation quality assessment for secondary treatment plans

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

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

Vasily Paraketsov