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:
Nov. 04, 2025

Filed:

Dec. 22, 2023
Applicant:

Anumana, Inc., Cambridge, MA (US);

Inventors:

Abhijith Chunduru, Bengaluru, IN;

Uddeshya Upadhyay, Bengaluru, IN;

Suthirth Vaidya, Bengaluru, IN;

Sai Saketh Chennamsetty, Bengaluru, IN;

Arjun Puranik, San Jose, CA (US);

Assignee:

Anumana, Inc., Cambridge, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/00 (2006.01); A61B 34/10 (2016.01); G06T 7/55 (2017.01); G06T 7/60 (2017.01); G06T 7/70 (2017.01); G06T 19/20 (2011.01); G16H 30/20 (2018.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
G06T 17/00 (2013.01); A61B 34/10 (2016.02); G06T 7/55 (2017.01); G06T 7/60 (2013.01); G06T 7/70 (2017.01); G06T 19/20 (2013.01); G16H 30/20 (2018.01); G16H 50/50 (2018.01); A61B 2034/105 (2016.02); G06T 2207/10081 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30101 (2013.01); G06T 2210/41 (2013.01); G06T 2219/2012 (2013.01);
Abstract

An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.


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