Align Technology, Inc.

Genoa Color Technologies Ltd.

Samsung Display Co., Ltd.

Orbotech Limited

Doron Malka

Maayan Moshe

Ofer Saphier

Avi Kopelman

Ido Tishel

Shai Ayal

Gilad Elbaz

Eric E Kuo

Michael Sabina

Assaf Weiss

Eliahou Franklin Nizard

Yossef Atiya

Erez Lampert

Moshe Ben-Chorin

Adi Levin

Pavel Agniashvili

Jonathan Coslovsky

Avraham Zulti

Shmuel Roth

Shai Farkash

Moti Ben-Dov

Dan Eliav

Ran Katz

Edi Fridman

Maxim Volgin

Inna Karapetyan

Pavel Veryovkin

Dina Bova

Christopher Eric Cramer

Chad Clayton Brown

Yun Gao

Roman Gudchenko

Yosi Moalem

Raphael Levy

David Fisch

A method of intraoral scanning includes receiving a first one or more intraoral scans of a patient's oral cavity; automatically determining, based on processing of the first one or more intraoral scans, a first scanning role associated with the first one or more intraoral scans, wherein the first scanning role is a first one of an upper dental arch role, a lower dental arch role or a bite role; and determining a first three-dimensional surface associated with the first scanning role.

Smart scanning for intraoral scanners

Methods and apparatuses for generating and displaying a model of a subject's teeth. Described herein are intraoral scanning methods and apparatuses for generating a three-dimensional model of a subject's intraoral region (e.g., teeth). These methods and apparatuses may be used for identifying and evaluating lesions, caries and cracks in the teeth.

Intraoral scanning apparatus

Methods and apparatuses (e.g., systems) for assessing oral health. In some examples, one or more periodontal pockets in a 3D model of a dental arch may be identified. The digital 3D model may be generated from scan data collected from an oral scan, where the scan data may include 3D surface data and one or more of: color image data, near-infrared (NIR) data, and fluorescence imaging data. A periodontal pocket depth may be determined for each periodontal pocket using a trained machine learning model, which may be trained on image data including previous 3D surface data and one or more of: previous color image data, previous NIR data, and previous fluorescence imaging data. An indicator of the determined periodontal pocket depth may be displayed on one or more 2D images and/or the digital 3D model of the subject's dental arch.

Noninvasive multimodal oral assessment systems

Described herein are intraoral scanning apparatuses comprising visible light and near-infrared (near-IR) light images from a patient's dentition. These apparatuses may display a 3D model of the patient's dental arch and a plurality of images of the patient's dental arch taken from different angles and positions relative to the patient's dental arch, and may both generate and display a two-dimensional (2D) image based on a relative angle and position between the indicator relative to the 3D model of the patient's dental arch.

Diagnostic intraoral scanning apparatuses

Methods and apparatuses (including systems and devices), including computer-implemented methods for segmenting, correcting and/or modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc., that may be selective and/or collectively digitally manipulated. In some implementations, artificial intelligence uses libraries of labeled 2D images and 3D dental models to learn how to segment a 3D dental model of a subject's oral cavity using 2D images, height map and/or other data and projection values that relate the 2D images to the 3D model. As noted herein, the dental classes can include a variety of intra-oral and extra-oral objects and can be represented as binary values, discrete values, a continuum of height map data, etc. In some implementations, several dental classes are predicted concurrently.

Apparatuses and methods for three-dimensional dental segmentation using dental image data

Intraoral scanning systems configured to perform computer-implemented methods for identifying dental features. The methods may include engaging a trained machine learning network to identify one or more dental features in one or more regions of a patient's dentition. Each of the dental features may be present in at least one of multiple records. Each of the records may include multiple images of the patient's dentition and may be taken with a different imaging modality. The confidence score may be determined or adjusted using aD model of the patient's dentition. The confidence score may be determined or adjusted based on the region of the patient's dentition within each record. The methods may include displaying an indicator of each dental feature that has a confidence score that is above a threshold on theD model of the patient's dentition, and modifying the display as a user adjusts the threshold.

Diagnostic intraoral methods and apparatuses

A processing device may receive a plurality of intraoral scans of the patient that were generated by an intraoral scanner, determine a three-dimensional surface of at least a portion of one or more dental arch of the patient using the plurality of intraoral scans, automatically determine whether a restorative dental object is represented in at least one of the three-dimensional surface or one or more intraoral scans of the plurality of intraoral scans, and automatically generate a prescription for treating the one or more teeth based at least in part on at least one of a) a presence or absence of a restorative dental object in at least one of the three-dimensional surface or the one or more intraoral scans or b) a location of the restorative dental object in the one or more dental arch of the patient.

Automatic generation of prosthodontic prescription

A method of generating a 3D model includes: determining a 3D surface comprising a preparation tooth using a first plurality of intraoral scans generated by an intraoral scanner at a first time; receiving one or more additional intraoral scans of the preparation tooth that were generated by the intraoral scanner at a second time; determining a change to one or more portions of the preparation tooth between the three-dimensional surface and the one or more additional intraoral scans; determining, based at least in part on the change to the preparation tooth, whether to use a) the 3D surface, b) data from the one or more additional intraoral scans or c) a combination of the 3D surface and the data from the one or more additional intraoral scans to depict one or more portions of the preparation tooth; and generating the 3D model comprising the preparation tooth.

Automatic determination of workflow for restorative dental procedures

A processing device receives a plurality of intraoral scans of a dental arch. The processing device determines, from the plurality of intraoral scans, a first set of intraoral scans that depict a restorative object and a second set of intraoral scans that fail to depict any restorative object. The processing device generates a multi-resolution three-dimensional model of the dental arch, wherein a first portion of the multi-resolution three-dimensional model is generated from the first set of intraoral scans, has a first resolution and represents the restorative object, and wherein a second portion of the multi-resolution three-dimensional model is generated from the second set of intraoral scans, has a second resolution and represents a remainder of the dental arch, wherein the first resolution is greater than the second resolution.

Automatic generation of multi-resolution 3d model including restorative object

Methods and apparatuses for assessing oral health and automatically providing diagnosis of one or more oral diseases. Described herein are intraoral scanning methods and apparatuses for collecting and analyzing image data and to detect and visualize features within image data that are indicative of oral diseases or conditions, such as gingival inflammation or oral cancer. These methods and apparatuses may be used for identifying and evaluating lesions, redness and inflammation in soft tissue and caries and cracks in the teeth. The methods can include training a machine learning model and using the trained machine learning model to provide a diagnosis of an oral disease or condition based on image data collected using multiple scanning modes of an intraoral scanner.

Noninvasive multimodal oral assessment and disease diagnoses apparatus and method

Systems, methods, and/or computer-readable media described herein provide technical solutions to aid in the color matching of dental restorations. These systems, methods and/or computer readable media may receive a three-dimensional (3D) model of a tooth, where the 3D model includes a plurality of voxels, set each voxel to a starting optical property, and form a 3D visible light model from the 3D model by iteratively adjusting the optical property of each voxel based on optical properties measured from a scan of the tooth, where the 3D visible light model provides information to generate a color matched dental restoration.

Tooth shading, transparency and glazing

Intraoral scanning systems for displaying 3D models of a patient's dental arch and near-IR images from the dental arch. These systems may include a wand with one or more image sensors and processors for processing the 3D models and near-IR information. The 3D models may include surface (e.g., color) information and the near-IR images may include information on internal structures, including enamel and dentin.

Intraoral scanning systems

Growing community of inventors

Tel Aviv, Israel

Doron Malka