James R. Glidewell Dental Ceramics, Inc.

Siemens Audiologische Technik Gmbh

Siemens Aktiengesellschaft

Siemens Corporation

Personics Holdings, Inc.

Siemens Hearing Instruments, Inc.

Paramatters Inc.

Sergei Azernikov

Sergey Vladimirovich Nikolskiy

Tong Fang

Shawn Andrews Ramirez

Dan Uri Lior

Fedor Chelnokov

Sajjad Hussain Baloch

Steve Edward Benson

Suraj Ravi Musuvathy

Alexander Zouhar

Rupen Melkisetoglu

Andrey Tolstov

Steven Wayne Goldstein

Gozde Unal

Fred McBagonluri

Claus Bahlmann

Artem Boltyenkov

Gang Li

Avi N Reichental

Hui Xie

Andreas Reh

Michael Bogomolny

Siegfried Fuchs

Konrad Sickel

Vojtech Bubnik

Simon Karpenko

Steven Edward Benson

Ruirui Jiang

A computer-implemented method/system of determining a trim-line includes receiving a 3D digital dental model; combining the one or more virtual teeth; determining a distance field; determining a first iso-line; determining a back distance field; determining a second iso-line to generate a trim-line. A computer-implemented method/system of segmenting the 3D digital dental model includes receiving a 3D digital dental model; determining an initial virtual tooth location of one or more virtual teeth; constructing a virtual crown line; determining a long axis of one or more virtual teeth; constructing a virtual root line; determining a virtual tooth virtual bounding box; performing an initial segmentation; performing final segmentation.

Automatic determination of trim-line for aligners

A computer-implemented method and system of digitally segmenting teeth in a digital model comprises generating a panoramic image from a 3D digital model of a patient's dentition, labeling, using a first trained neural network, the panoramic image to provide a labeled panoramic image, mapping the labeled panoramic image to corresponding coarse digital surface triangle labels in the 3D digital model to provide a labeled 3D digital model, and segmenting the labeled 3D digital model to provide a segmented 3D digital model. A computer-implemented method and system of generating a panoramic image comprises determining, using a trained neural network, digital tooth bounding region(s) corresponding to digital teeth from a 2D depth map of a patient's dentition, connecting digital tooth bounding region(s) by a spline, determining sampled digital surface points from the sampled spline points; and determining associated digital surface points corresponding to each sampled digital surface point.

Teeth segmentation using neural networks

A computer-implemented method/system/instructions of automatic margin line proposal includes receiving a 3D digital model of at least a portion of a jaw, the 3D digital model including a digital preparation tooth; determining, using a first trained neural network, an inner representation of the 3D digital model; and determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model.

Neural network margin proposal

Computer-implemented methods for generating a 3D dental prosthesis model are disclosed herein. The methods comprise training a deep neural network to generate a first 3D dental prosthesis model using a training data set; receiving a patient scan data representing at least a portion of a patient's dentition; and generating, using the trained deep neural network, the first 3D dental prosthesis model based on the received patient scan data.

Dental CAD automation using deep learning

Methods and apparatuses can include generating computer 3D models of an object, by registering two or more scans of physical models of an object. The scans may be 3D scans registered by a curve-based registration process. A method can include generating a 3D model of a portion of a patient's oral anatomy that can be used in dental restoration design. Scanning workflows can include scanning physical models of an object to obtain a 3D model. A scanner can scan physical models of a patient's dentition to obtain scans used to generate a 3D model of a patient's dentition.

Method and apparatus for generation of 3D models with applications in dental restoration design

Methods and apparatus are provided for generating computer 3D models of an object, by registering two or more scans of physical models of an object. The scans may be 3D scans registered by a curve-based registration process. A method is provided for generating a 3D model of a portion of a patient's oral anatomy for use in dental restoration design. Also provided are scanning workflows for scanning physical models of an object to obtain a 3D model. A scanner is provided for scanning physical models of a patient's dentition to obtain scans for generating a 3D model of a patient's dentition.

A computer-implemented method of recognizing dental information associated with a dental model of dentition includes training a deep neural network to map a plurality of training dental models representing at least a portion of each one of a plurality of patients' dentitions to a probability vector including probability of the at least a portion of the dentition belonging to each one of a set of multiple categories. The category of the at least a portion of the dentition represented by the training dental model corresponds to the highest probability in the probability vector. The method includes receiving a dental model representing at least a portion of a patient's dentition and recognizing dental information associated with the dental model by applying the trained deep neural network to determine a category of the at least a portion of the patient's dentition represented by the received dental model.

Computer-implemented methods of designing dental restorations and systems for performing the described methods are provided. In an embodiment, a method includes providing a virtual three dimensional representation of at least a portion of the patient's dentition that includes at least one preparation tooth, identifying a preparation margin on the virtual three dimensional representation, placing an arch form of a virtual tooth library in alignment with the virtual three dimensional representation, and proposing an initial restoration design based upon a tooth design obtained from the virtual tooth library.

Computer-implemented dental restoration design

A computer-aided design (CAD) model of an article for manufacture, together with user problem definition input including materials, constraints, load conditions, preferred manufacturing processes, etc. for an optimized design of the article or material microstructure are received via a prompt-and-response graphical user interface. Based on this information, one or more routines to iteratively optimize material distribution within said article or the material microstructure design are executed in order to achieve best performance for materials, loads, and constraints specified by the user. At the completion of the optimization process, the user is presented the optimized design in a CAD file describing the optimized design of the article for the preferred manufacturing process or a newly designed material microstructure, as appropriate.

Cognitive system for computer aided design

A method for compact and descriptive representation of teeth shape (or other anatomic shapes) in terms of characteristic curves and its application to generation of automatic designs within dental CAD software or other software is provided. In an embodiment, a method includes capturing tooth shape by a network of characteristic curves (i.e. margin lines). In an embodiment, a method includes compactly encoding curves as strings, which then can be indexed and searched efficiently by similarity. In an embodiment, a method includes retrieving high quality crown design proposals from a case repository based on similarity of margin lines.

Method for encoding of anatomic curves

A method for compact and descriptive representation of teeth shape, or other shapes, in terms of characteristic curves and its application to generation of automatic designs within dental CAD software or other software is provided. In an embodiment, a method includes capturing tooth shape by a network of characteristic curves, such as margin lines. In an embodiment, a method includes compactly encoding curves as strings, which then can be indexed and searched efficiently by similarity. In an embodiment, a method includes retrieving high quality crown design proposals from a case repository based on similarity of margin line.

Method and apparatus for shape analysis, storage and retrieval of 3D models with application to automatic dental restoration design

Methods and apparatus are provided for generating computer 3D models of an object, by registering two or more scans of physical models of an object. The scans may be 3D scans registered by a curve-based registration process. A method is provided for generating a 3D model of a portion of a patient's oral anatomy for use in dental restoration design. Also provided are scanning workflows for scanning physical models of an object to obtain a 3D model.

Methods and systems for designing an earpiece device are provided. The method includes receiving a plurality of images for a respective plurality of individuals. Each image includes at least one ear anatomy. For each image, a three-dimensional (3D) surface representing the at least one ear anatomy is extracted, to form a plurality of extracted surfaces corresponding to the plurality of images. At least one statistical measurement representative of at least a portion of the plurality of individuals is determined from among the plurality of extracted surfaces. At least one design parameter for the earpiece device is optimized based on the at least one statistical measurement, The earpiece device is formed using the optimized at least one design parameter.

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Irvine, CA, United States of America

Sergei Azernikov