Align Technology, Inc.

Guotu Li

Christopher Eric Cramer

Chad Clayton Brown

Phillip Thomas Harris

Yun Gao

Sebastien Hareng

Leela Parvathaneni

Sravani Gurijala

Rajiv Venkata

Svetozar Hubenov

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

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

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

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.

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.

Growing community of inventors

Durham, NC, United States of America

Guotu Li