Align Technology, Inc.

Other

Chad Clayton Brown

Christopher Eric Cramer

Yun Gao

Guotu Li

Pavel Agniashvili

Ofer Saphier

Maayan Moshe

Phillip Thomas Harris

Sebastien Hareng

Leela Parvathaneni

Sravani Gurijala

Rajiv Venkata

Svetozar Hubenov

Jonathan Coslovsky

Avi Kopelman

Adi Levin

Michael Sabina

Doron Malka

Samuel Blanco

Assaf Weiss

Shai Farkash

Dmitry Yurievich Chekh

Vladislav Andreevich Miryaha

Boris Aleksandrovich Vysokanov

Moti Ben-Dov

Roman Gudchenko

Avraham Zulti

Maxim Volgin

Alexander Raskhodchikov

Igor Makiewsky

Yingjie Li

Mikhail Minchenkov

Chao Shi

Ran Katz

Anton Spiridonov

A method of dental treatment may include receiving one or more photo parameters to define clinically acceptable criteria for a plurality of clinically relevant photos of a person's dentition. The clinically acceptable criteria may include at least a plurality of clinically acceptable positions and a plurality of clinically acceptable orientations of teeth relative to a camera. The method may also include gathering a plurality of image-capture rules to capture the plurality of clinically relevant photos. The plurality of image capture rules may be based on the one or more photo parameters. The method may also include providing first automated instructions to capture the plurality of clinically relevant photos of the person's dentition using the plurality of image-capture rules and capturing the plurality of clinically relevant photos with the camera in response to the first automated instructions.

Intelligent photo guidance for dentition capture

Systems and methods for scoring a post-treatment tooth position of a patient's teeth. Raw features of the patient's teeth may be determined from a 3D model of the patient's teeth. Engineered features may be created based on the raw features, which may be applied to a classifier trained to provide a post-treatment tooth position score for the patient's teeth. The post-treatment tooth position score may include a label for each tooth and an associated tooth position score and may be generated as output.

Machine learning scoring system and methods for tooth position assessment

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

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

Embodiments may include: receiving a facial image comprising a 2D depiction of a person's teeth at a first arrangement; receiving a tooth model comprising a 3D depiction of the person's teeth at a second arrangement; obtaining color information from the facial image, wherein the color information represents a color scheme of the person's teeth at the first arrangement; and generating a photo-realistic two-dimensional depiction of the person's teeth at the second arrangement using the 3D depiction of the person's teeth at the second arrangement and the color information obtained from the facial image.

Rendering of post treatment smile image

Embodiments include receiving image data comprising contours of teeth of an individual and generating a new image based on the image data and one or more parametric functions associated with tooth color that comprise a first variable for a first image axis and a second variable for a second image axis, wherein a shape of the teeth in the new image is based on the image data and a color of the teeth in the new image is based on applying the one or more parametric functions to at least a portion of the image data.

Smile prediction

A processing device receives an image and calculates a sharpness metric for a region of interest of the image. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

Selective image modification based on sharpness metric and image domain

In embodiments, a processing device generates a three-dimensional model of a dental site from scan data, the three-dimensional model comprising a representation of a tooth, wherein a portion of the three-dimensional model comprises an interfering surface that obscures a portion of the tooth. The processing device receives or generates an image of the tooth, wherein the image depicts the interfering surface. The processing device processes the image to generate a modified image, wherein the portion of the tooth that was obscured by the interfering surface in the image is shown in the modified image. The processing device updates the three-dimensional model of the dental site by replacing, using the modified image, the portion of the three-dimensional model that comprises the interfering surface that obscures the portion of the tooth, wherein the portion of the tooth that was obscured in the three-dimensional model is shown in an updated three-dimensional model.

Automated, generation of dental features in digital models

Methods and systems are described that mark and/or correct margin lines and/or other features of dental sites. In one example a three-dimensional model of a dental site is generated from intraoral scan data of the dental site, the three-dimensional model comprising a representation of a preparation tooth. An image of the preparation tooth is received or generated. Data from the image is processed using a trained machine learning model that has been trained to identify margin lines of preparation teeth, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the image, a probability that the pixel depicts a margin line. The three-dimensional model of the dental site is then updated by marking the margin line on the representation of the preparation tooth based on the probability map.

Automated detection, generation and/or correction of dental features in digital models

A method for dental treatment may include receiving photos of a patient's dentition, gathering treatment parameters for at treatment plan. The treatment parameters may represent attributes of the treatment plan selected by a doctor for the patient's dentition of a patient. The method may include generating, using the photos and the treatment parameters, one or more intelligent guidance rules to guide application of at least a portion of the treatment parameters to the patient's dentition, generating instructions to apply the intelligent patient guidance rules to the patient's dentition, and providing the instructions to the patient to apply the at least a portion of the treatment plan in accordance with the intelligent patient guidance rules.

Systems, apparatus, and methods for remote orthodontic treatment

A method for treatment-based photo guidance may include receiving a treatment plan, determining the movement of a first one or more teeth based on the movement of tooth models in the treatment plan, and determining one or more photo views for capturing the movement of the first one or more teeth.

Treatment-based image capture guidance

A method for image-based orthodontic treatment may include receiving image data of a patient's dentition and an orthodontic appliance, identifying, from the image data, the orthodontic appliance, calculating a misalignment height of a misalignment of the orthodontic appliance with respect to the patient's dentition, determining whether the misalignment height satisfies a misalignment threshold, and in response to satisfying the misalignment threshold, providing a notification.

Image-based aligner fit evaluation

A system includes an intraoral scanner and a computing device. The intraoral scanner generates an intraoral scan of a dental site. The computing device processes an input comprising data from the intraoral scan using a trained machine learning model that has been trained to classify regions of dental sites, wherein the trained machine learning model generates an output comprising, for each point in the intraoral scan, an indication as to whether the point belongs to a first dental class that represents excess material. The computing device determines, based on the output, one or more points in the intraoral scan that are classified as excess material. The computing device then hides or removes, from at least one of the intraoral scan or a virtual three-dimensional (3D) model generated using the intraoral scan, data for the one or more points that are classified as excess material.

Intraoral scanning system with excess material removal based on machine learning

A method of dental treatment may include receiving photos of a person's dentition, identifying a stage of a treatment plan administered to the person's dentition, gathering a three-dimensional (3D) model of the person's dentition corresponding to the stage of the treatment plan, projecting attributes of the 3D model of the person's dentition onto an image plane to get a projected representation of the person's dentition at the stage of the treatment plan, comparing the photos to the projected representation to derive an error image representing the comparison, and analyzing the error image for discrepancies, wherein the discrepancies represent one or more deviations of the person's dentition from the stage of the treatment plan.

Image based dentition tracking

Provided herein are systems and methods for scoring a post-treatment tooth position of a patient's teeth. 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 the post-treatment tooth position of the patient's teeth.

Growing community of inventors

Cary, NC, United States of America

Chad Clayton Brown