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:
Feb. 11, 2025

Filed:

Nov. 06, 2023
Applicant:

Heartflow, Inc., Mountain View, CA (US);

Inventors:

Leo Grady, Darien, CT (US);

Michiel Schaap, Oegstgeest, NL;

Edward Karl Hahn, III, Foster City, CA (US);

Assignee:

HeartFlow, Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06T 11/00 (2006.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 11/003 (2013.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06N 20/00 (2019.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30104 (2013.01); G06T 2207/30172 (2013.01);
Abstract

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.


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