Align Technology, Inc.

Yun Gao

Chad Clayton Brown

Christopher Eric Cramer

Phillip Thomas Harris

Sebastien Hareng

Leela Parvathaneni

Sravani Gurijala

Rajiv Venkata

Svetozar Hubenov

Yingjie Li

Chao Shi

Guotu Li

Adi Levin

Ofer Saphier

Maayan Moshe

Doron Malka

Pavel Agniashvili

Jonathan Coslovsky

Roman Gudchenko

Avraham Zulti

Andrey Bushev

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

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 include receiving image data comprising a representation of teeth, wherein the image data comprises a set of pixel locations for the teeth and depth values associated with the pixel locations in the set of pixel locations. The method further includes generating new image based on applying one or more functions to the image data, wherein the one or more functions adjust one or more color channels of the set of pixel locations based at least in part on the depth values.

Generating teeth images colored based on teeth depth

Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth and gingiva based on the first 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the parametric 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with gingiva and teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Visual presentation of gingival line generated based on 3D tooth model

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.

Image based dentition tracking

A method for dental treatment may include receiving photos capturing a state of a patient's dentition of a patient at a specific time, retrieving a first treatment plan to treat the patient's dentition, identifying an intended arrangement of the first treatment plan at the specific time, evaluating the photo parameters of the photos to generate alignment data to align the intended arrangement of the first treatment plan and the photos, generating an alignment mesh using the alignment data, the alignment mesh comprising a three-dimensional (3D) mesh representation of the patient's dentition at the specific time, performing an evaluation of the first treatment plan for modifications using the alignment mesh, and identifying proposed modifications to the first treatment plan based on the evaluation.

Image based orthodontic treatment refinement

A method includes determining depth values associated with a first set of pixel locations in a first image of a mouth. One or more function is generated for one or more color channels based on intensities of the one or more color channels at the first set of pixel locations and depth values associated with the first set of pixel locations. Image data comprising a new representation of the teeth is received, wherein the image data comprises a second set of pixel locations and new depth values associated with the second set of pixel locations. A new image is generated based on the image data and the one or more functions.

Generation of images with tooth color determined using depth information

Methods and apparatuses (including systems and devices) for modifying a three-dimensional (3D) model of a subject's oral cavity to determine individual components such as teeth, gingiva, tongue, palate, etc. In some implementations one or more automated machine learning agents may modify one or more subsets of 3D models of the subject's oral cavity using height map data to identify, segment and/or modify to mesh regions of a 3D model constructed from a plurality of 2D images of the subject's dental cavity.

Artificially intelligent systems to manage virtual dental models using dental images

A method includes determining depth values associated with a first set of pixel locations in a first image of a mouth. A first function is generated for a color channel based on intensities of the color channel at the first set of pixel locations and depth values associated with the first set of pixel locations. Image data comprising a new representation of the teeth is received, wherein the image data comprises a second set of pixel locations and new depth values associated with the second set of pixel locations. A new image is generated based on the image data and the first function, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the first function to the second set of pixel locations for the teeth and the new depth values.

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Cary, NC, United States of America

Yun Gao