Align Technology, Inc.

Maayan Moshe

Ofer Saphier

Avi Kopelman

Shai Ayal

Doron Malka

Gilad Elbaz

Adi Levin

Yossef Atiya

Michael Sabina

Ido Tishel

Assaf Weiss

Eliahou Franklin Nizard

Eric E Kuo

Erez Lampert

Shai Farkash

Tal Verker

Pavel Agniashvili

Jonathan Coslovsky

Moti Ben-Dov

Avraham Zulti

Maxim Volgin

Chad Clayton Brown

Ran Katz

Edi Fridman

Inna Karapetyan

Pavel Veryovkin

Dina Bova

Christopher Eric Cramer

Yun Gao

Roman Gudchenko

Raphael Levy

Alexander Raskhodchikov

Igor Makiewsky

Yosi Moalem

Yingjie Li

Phillip Thomas Harris

Sergei Ozerov

Sergey Gagarin

Maurice K Carrier

Ozi Marom

Sergey Chezhin

Konstantin Kryzhanovsky

Ilya Arkhipovskiy

Dental imaging attachment for a smartphone

Embodiments relate to techniques for real-time and post-scan visualization of intraoral scan data, which may include 3D images, 3D scans, 3D surfaces and/or 3D models. In one embodiment, an intraoral scanning system comprises a plurality of image sensors to periodically generate a set of intraoral two-dimensional (2D) images, wherein for each set of intraoral 2D images each image sensor of the plurality of image sensors is to generate an intraoral 2D image, and wherein relative positions and orientations of the plurality of image sensors are known. The intraoral scanning system further comprises a computing device, wherein the computing device is to perform the following for each set of intraoral 2D images: generate a combined intraoral image based on merging the set of intraoral 2D images together during scanning; and output the combined intraoral image to a display.

Viewing trajectory for 3D dental model

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

A method of multimodal scanning may include generating surface scan data of an intraoral structured using structured light. The method may include generating volumetric scan data of an internal structure of the intraoral structure with OCT scanning. The OCT scan data may be aligned with the surface scan data. A three-dimensional volumetric model of the patient's dentition may be generated based on the aligned OCT scan data and the surface scan data.

Multimodal intraoral scanning

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

An intraoral scanner includes a probe with a sensing face, patterned light sources that are coupled to the probe, un-patterned light sources coupled to the probe, near infrared (NIR) light sources couple to the probe, cameras coupled to the probe, and a processing device coupled to the probe. The processing device is configured to control an operation of the patterned light sources, the un-patterned light sources, and the NIR light sources.

Intraoral scanner with illumination sequencing and controlled polarization

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

Provided herein are devices and methods generating synthetic views of a subject's teeth. Methods and processes are provided to image the subject's teeth with a dental scan. Methods and processes are also provided to automatically 3D render the subject's teeth with the scan images. Methods and apparatuses are also provided to generate simulated synthetic views of the subject's dentition from various perspectives.

Dental panoramic views

In embodiments, a processing device detects a margin line in a model of a preparation from one or more images. The processing device determines, for each segment of a plurality of segments of the margin line, a quality score for the segment. The processing device determines that a segment of the margin line has a quality score that is below a quality threshold, wherein the segment of the margin line was generated based on a first set of images. Responsive to determining that the segment of the margin line has the quality score that is below the quality threshold, the processing devices updates the three-dimensional model of the preparation by replacing a portion of the three-dimensional model associated with the segment of the margin line with image data from at least one of a new image or a second set of images.

Automated detection, generation and/or correction of margin lines in digital models

Growing community of inventors

Raanana, Israel

Maayan Moshe