Align Technology, Inc.

Christopher Eric Cramer

Chad Clayton Brown

Guotu Li

Yun Gao

Phillip Thomas Harris

Sebastien Hareng

Leela Parvathaneni

Sravani Gurijala

Rajiv Venkata

Svetozar Hubenov

Jeeyoung Choi

Roman Gudchenko

Adi Levin

Ofer Saphier

Maayan Moshe

Doron Malka

Samuel Blanco

Pavel Agniashvili

Jonathan Coslovsky

Dmitry Yurievich Chekh

Vladislav Andreevich Miryaha

Boris Aleksandrovich Vysokanov

Avraham Zulti

Yingjie Li

Chao Shi

Anton Spiridonov

Eric P Meyer

Sergey Grebenkin

Aleksandr Sergeevich Karsakov

Alexander Vovchenko

Dmitry Guskov

Vasily Paraketsov

Mikhail Gorodilov

Denis Durdin

Roman Solovyev

Dmitrii Ischeykin

Ivan Potapenko

Elizaveta Ulianenko

Nikolay Zhirnov

Aleksandr Anikin

Mikhail Toporkov

Anton Baskanov

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

Provided herein are methods and apparatuses for analyzing a patient's dental arches in order to generate a treatment plan for the dentition. In particular described herein are methods and apparatuses for determining accurate standardized tooth numbering even when there are missing and/or supernumerary teeth.

Dental arch analysis and tooth numbering

Provided herein are systems and methods for optimizing a 3D model of an individual's teeth. A 3D dental model may be reconstructed from 3D parameters. A differentiable renderer may be used to derive a 2D rendering of the individual's dentition. 2D image(s) of an individual's dentition may be obtained, and features may be extracted from the 2D image(s). Image loss between the 2D rendering and the 2D image(s) can be derived, and back-propagation from the image loss can be used to calculate gradients of the loss to optimize the 3D parameters. A machine learning model can also be trained to predict a 3D dental model from 2D images of an individual's dentition.

Methods and systems for forming a three-dimensional model of dentition

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

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

The disclosed systems and methods for anonymizing clinical data may include receiving representation data corresponding to a body part. The representation data may include a clinically relevant region and an anonymization region. The method may include extracting, from the representation data, clinical representation data corresponding to the clinically relevant region of the representation data and generating artificial representation data corresponding to the anonymization region of the representation data. The method may further include creating, based at least on the clinical representation data and the artificial representation data, anonymized representation data that substantially preserves the clinically relevant region. Various other methods, systems, and computer-readable media are also disclosed.

Clinically relevant anonymization of photos and video

Computer readable media for predicting a tooth shape for at least partially un-erupted teeth and dental appliances formed therefrom. In some examples, methods include generating orthoscopic views of virtual representations of an identified tooth type in different orthogonal directions, representing bounding shapes around the orthoscopic views, and applying principal component analysis on the bounding shapes to predict a tooth shape. In some examples, methods include generating a spherical harmonic signature for virtual representations of an identified tooth type, calculating a distance between spherical harmonic signatures to predict the tooth shape. The predicted tooth shape of the at least partially un-erupted tooth may be incorporated into virtual dentition model(s) of a dentition in accordance with an orthodontic treatment plan for treating the dentition.

Predicting shapes of at least partially un-erupted teeth

Provided herein are methods and apparatuses for recommending tooth leveling (e.g., one or more of anterior leveling, posterior leveling, and arch-shape recommendations) for orthodontic devices and methods for patients. Factors involved in the leveling of teeth can include symmetry, doctor preferences, preferences regarding gender, the country in which the patient is being treated, and other issues related to the aesthetics of the mouth and arch shape. The device can comprise an aligner configured to fit over a patient's teeth. Methods of designing and manufacturing aligners based on leveling recommendations are also provided.

Apparatuses and methods assisting in dental therapies

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

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 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.

Growing community of inventors

Durham, NC, United States of America

Christopher Eric Cramer