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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.

Patent No.:

US 12573036 B1

PDFFull Text
Date of Patent:
Mar. 10, 2026

Filed:

Jul. 19, 2023

Prediction of major adverse cardiovascular events (mace) from ai analysis of pericoronary fat in ct images

Applicants:

Case Western Reserve University, Cleveland, OH (US);

University Hospitals Cleveland Medical Center, Cleveland, OH (US);

Inventors:

David L. Wilson, Cleveland Heights, OH (US);

Ammar Hoori, Westlake, OH (US);

Tao Hu, Cleveland, OH (US);

Yingnan Song, Cleveland, OH (US);

Hao Wu, Cleveland, OH (US);

Juhwan Lee, Westlake, OH (US);

Sadeer Al-Kindi, Cleveland, OH (US);

Sanjay Rajagopalan, Cleveland, OH (US);

Assignees:

Case Western Reserve University, Cleveland, OH (US);

University Hospitals Cleveland Medical Center, Cleveland, OH (US);

Attorney:

Eschweiler & Potashnik, LLC

Primary Examiner:

Edward Park

Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/30 (2017.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); G06V 10/766 (2022.01); G06V 10/774 (2022.01); G06V 20/50 (2022.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/30 (2017.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); G06V 10/766 (2022.01); G06V 10/774 (2022.01); G06V 20/50 (2022.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30048 (2013.01); G06V 2201/031 (2022.01);
Abstract

The present disclosure, in some embodiments, relates to a method of generating a prognosis for a patient. The method includes accessing automatically segmented pericoronary adipose tissue (PCAT) corresponding to a patient within an electronic memory. A plurality of non-confounding PCAT features are generated by measuring values of Hounsfield units for an imaging unit within the PCAT. The measured values of the Hounsfield units are predominately free of iodine confounding and artifacts. The plurality of non-confounding PCAT features are provided to a regression model. The regression model is configured to generate a prognosis for the patient using the plurality of non-confounding PCAT features

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