US 11,922,627 B2
Systems, devices, and methods for non-invasive image-based plaque analysis and risk determination
James K. Min, Denver, CO (US); James P. Earls, Fairfax Station, VA (US); Shant Malkasian, Pasadena, CA (US); Hugo Miguel Rodrigues Marques, Lisbon (PT); Chung Chan, Northbrook, IL (US); and Shai Ronen, Westminster, CO (US)
Assigned to CLEERLY, INC., Denver, CO (US)
Filed by Cleerly, Inc., Denver, CO (US)
Filed on Aug. 23, 2023, as Appl. No. 18/454,462.
Application 18/454,462 is a continuation of application No. 18/179,921, filed on Mar. 7, 2023.
Claims priority of provisional application 63/381,210, filed on Oct. 27, 2022.
Claims priority of provisional application 63/368,293, filed on Jul. 13, 2022.
Claims priority of provisional application 63/365,381, filed on May 26, 2022.
Claims priority of provisional application 63/364,084, filed on May 3, 2022.
Claims priority of provisional application 63/364,078, filed on May 3, 2022.
Claims priority of provisional application 63/362,856, filed on Apr. 12, 2022.
Claims priority of provisional application 63/362,108, filed on Mar. 29, 2022.
Claims priority of provisional application 63/269,136, filed on Mar. 10, 2022.
Prior Publication US 2023/0394663 A1, Dec. 7, 2023
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); A61B 5/00 (2006.01); G06T 7/62 (2017.01)
CPC G06T 7/0012 (2013.01) [A61B 5/7275 (2013.01); G06T 7/62 (2017.01); G06T 2207/10048 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10101 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10108 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30101 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A computer-implemented method of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis, the method comprising:
accessing, by a computer system, a medical image of a subject, wherein the medical image of the subject is obtained non-invasively;
analyzing, by the computer system, the medical image of the subject to identify one or more arteries;
identifying, by the computer system, one or more regions of plaque within the one or more coronary arteries;
analyzing, by the computer system, the identified one or more regions of plaque to identify one or more regions of low density non-calcified plaque based at least in part on density,
analyzing the one or more regions of low density non-calcified plaque, wherein the analysis of the one or more regions of low density non-calcified plaque comprises:
determining a distance from the one or more regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall;
determining a degree of embeddedness of the one or more regions of low density non-calcified plaque in one or more of non-calcified plaque or calcified plaque; and
determining a shape of the one or more regions of low density non-calcified plaque, wherein the shape of the one or more regions of low density non-calcified plaque is determined based at least in part by a machine learning algorithm, wherein the machine learning algorithm comprises a convolutional neural network trained on a set of medical images in which shapes of regions of plaque have been identified; and
determining, by the computer system, a risk of CAD of the subject based at least in part on the the determined distance, the determined degree of embeddedness, and the determined shape of the one or more regions of low density non-calcified plaque by comparison to one or more reference values for distances from regions of low density non-calcified plaque to lumen or vessel walls, embeddedness values for regions of low density non-calcified plaque, and shapes of regions of low density non-calcified plaque, wherein the references values are derived from a population with varying states of risk of CAD,
wherein the computer system comprises a computer processor and an electronic storage medium.