The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 12, 2023

Filed:

Mar. 17, 2021
Applicant:

Arbrea Labs Ag, Zurich, CH;

Inventors:

Endri Dibra, Zurich, CH;

Niko Benjamin Huber, Zurich, CH;

Assignee:

Align Technology, Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/00 (2006.01); G06T 17/20 (2006.01); G06T 15/20 (2011.01); G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G16H 50/70 (2018.01); G16H 50/50 (2018.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G16H 20/40 (2018.01); A61B 90/00 (2016.01); A61B 3/10 (2006.01); A61B 34/10 (2016.01); G06F 18/214 (2023.01); A61B 17/00 (2006.01);
U.S. Cl.
CPC ...
G06T 17/00 (2013.01); A61B 34/10 (2016.02); A61B 90/361 (2016.02); A61B 90/37 (2016.02); G06F 18/214 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06T 15/205 (2013.01); G06T 17/20 (2013.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 2017/00796 (2013.01); A61B 2034/104 (2016.02); A61B 2034/105 (2016.02); A61B 2090/363 (2016.02); A61B 2090/367 (2016.02); A61B 2090/373 (2016.02); G06T 2200/24 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30068 (2013.01); G06T 2210/41 (2013.01);
Abstract

A medical image might be generated for a patient image using a convolutional neural network trained on prior patient pre-procedure and post-procedure 2D images. A method might generate 3D models from pre-procedure 2D images and from post-procedure 2 images, and train the convolutional neural network with training images being the 3D models, to generate at least one 3D model of the present patient from the 2D image of the present patient, apply the 3D model of the present patient to the convolutional neural network in an inference stage, and apply patient-specific parameters derived from the proposed surgical procedure as a second input to the convolutional neural network in the inference stage to generate an inferred post-surgery 3D model of the present patient given the patient-specific parameters.


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