Align Technology, Inc.

Vladislav Andreevich Miryaha

Boris Aleksandrovich Vysokanov

Dmitry Yurievich Chekh

Samuel Blanco

David Patrick Lopes

Rene M Sterental

Yury A Brailov

Jason Ramos

Evgenii Vladimirovich Karnygin

Pavel Pokotilov

Artem Kuanbekov

Dmitriy Konstantinovich Ten

Christopher Eric Cramer

Chad Clayton Brown

Yingjie Li

Chao Shi

Andrey Bushev

Valery Prokoshev

Elena Agilina

Vera Vladimirovna Kadrul

Anastasia Chikhanova

Travis Harrison

Akhil Deshpande

Aleksei Lisitsin

Systems and methods for planning a treatment for a patient's teeth are provided. In some embodiments, a method includes receiving input data representing an initial tooth arrangement of a patient's teeth. The method can include determining a target tooth arrangement for the patient's teeth, the target tooth arrangement including a change in mass of at least one tooth. The method can also include generating a plurality of intermediate tooth arrangements configured to adjust the patient's teeth from the initial tooth arrangement toward the target tooth arrangement. The method can further include generating instructions to output a visualization showing a difference in tooth mass between at least one intermediate tooth arrangement of the plurality of intermediate tooth arrangements and the target tooth arrangement.

Systems and methods for orthodontic and restorative treatment planning

Provided herein are orthodontic systems and methods for automatically segmenting a person's teeth. Systems, methods and processes are provided to properly segment the teeth of a person's teeth from an image of the person's face showing at least some of the person's teeth (e.g., dental arch). Methods and systems are provided to automatically detect dental edges after a dental scan. Also described herein are methods and systems for generating a simulated image of the person's face from the final segmentation of the person's teeth in which the segmented teeth have been moved from their original position, including a new position based on an orthodontic treatment plan.

Tooth segmentation based on anatomical edge information

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

Methods and systems including: receiving one or more 2D images of a patient's face including a depiction of the patient's teeth in an initial arrangement; extracting teeth contours of the patient's teeth from within the mouth region of the one or more received 2D images of the patient's face; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient's teeth based on the one or more received 2D images; generating a realistic composite 2D image depicting the patient's face with the 2D rendering depicting the post-treatment arrangement of the patient's teeth; and sending the realistic composite 2D image for display on a user interface of an output device.

Generating a virtual patient depiction of an orthodontic treatment

A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

Automated 2D/3D integration and lip spline autoplacement

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

Photo realistic rendering of smile image after treatment

Growing community of inventors

Engels, Russia

Vladislav Andreevich Miryaha