IP Library › Granted Patent US 11,672,497
Granted Patent B2
US 11,672,497 · App. 18/148,347 · Granted Jun 13, 2023

Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking

Inventors: James K. Min (Denver, CO); James P. Earls (Fairfax Station, VA); Hugo Miguel Rodrigues Marques (Lisbon, PT)
Assignee: CLEERLY. INC.
A61B6/504A61B5/0066A61B5/0075A61B5/055A61B5/7267A61B5/742A61B5/7475A61B6/032A61B6/037A61B6/481A61B6/5205A61B8/12A61B8/14A61K49/04G06F18/10G06T7/0012G06V10/20G06V10/245G06V10/761G06V10/764G06V10/82G06T2207/10081G06T2207/10088G06T2207/10101G06T2207/10132G06T2207/20081G06T2207/30048G06T2207/30101G06V10/247
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Quick Facts
Patent No.
US 11,672,497
App. No.
18/148,347
Filed
Dec 29, 2022
Granted
Jun 13, 2023
Kind
B2
Examiner
LU, TOM Y
Art Unit
2667
USPC
382/131
Abstract

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Claims (62)

1. A computer-implemented method for assessing a need for coronary revascularization for a subject based on image analysis of a non-invasive medical image of the subject and analysis of one or more plaque parameters or vascular parameters derived from the image analysis, the method comprising:

accessing, by a computer system, a medical image comprising one or more regions of one or more coronary arteries of a subject, the medical image obtained non-invasively;

analyzing, by the computer system, the medical image to identify stenosis or luminal narrowing present in the one or more regions of the one or more coronary arteries of the subject, wherein the stenosis or luminal narrowing is identified based at least in part on a lumen of the one or more regions of the one or more coronary arteries, wherein the identified stenosis or luminal narrowing is configured to be used to determine a risk of coronary artery disease for the subject (CAD);

performing, by the computer system, further analysis of the medical image for atherosclerosis and ischemia when the subject is suspected to have CAD,

wherein analysis for atherosclerosis comprises:

identifying one or more regions of plaque in the one or more regions of the one or more coronary arteries of the subject; and

generating one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque, the one or more quantified plaque parameters comprising one or more of total plaque volume, volume of low-density non-calcified plaque, volume of non-calcified plaque, or volume of calcified plaque, and

wherein analysis for ischemia comprises:

generating one or more vascular parameters of the one or more regions of the one or more coronary arteries, the one or more vascular parameters comprising one or more of vascular morphology, lumen wall, or vessel wall; and

determining a risk of ischemia for the one or more regions of the one or more coronary arteries based at least in part on the generated one or more vascular parameters; and

generating, by the computer system, a graphical representation of the analysis for atherosclerosis and the analysis of ischemia for the one or more regions of the one or more coronary arteries, wherein the graphical representation is configured to be used to facilitate determination of a need for coronary revascularization for the subject,

wherein the computer system comprises a computer processor and an electronic storage medium.

2. The computer-implemented method of claim 1 , wherein the graphical representation comprises a cross-sectional view and a multiplanar view of the one or more regions of the one or more coronary arteries.

3. The computer-implemented method of claim 2 , wherein the graphical representation further comprises the identified one or more regions of plaque in the one or more regions of the one or more coronary arteries.

4. The computer-implemented method of claim 3 , wherein the graphical representation further comprises a three-dimensional artery tree view of the one or more regions of the one or more coronary arteries.

5. The computer-implemented method of claim 4 , wherein the cross-sectional view, the multiplanar view, and the three-dimensional artery tree view of the one or more regions of the one or more coronary arteries are configured to be viewed simultaneously.

6. The computer-implemented method of claim 1 , wherein the graphical representation further comprises a representation of the stenosis or luminal narrowing identified in the one or more regions of the one or more coronary arteries.

7. The computer-implemented method of claim 1 , wherein the graphical representation comprises a representation of one or more of total plaque volume, volume of low-density non-calcified plaque, volume of non-calcified plaque, or volume of calcified plaque.

8. The computer-implemented method of claim 1 , wherein the subject is suspected to have CAD based at least in part on the risk of CAD determined based on the identified stenosis or luminal narrowing.

9. The computer-implemented method of claim 1 , wherein analyzing the medical image to identify stenosis or luminal narrowing present in the one or more regions of the one or more coronary arteries of the subject comprises identifying a sum of all stenoses or luminal narrowings present across a length of a vessel.

10. The computer-implemented method of claim 1 , wherein the risk of ischemia is further determined based at least in part on the one or more quantified plaque parameters.

11. The computer-implemented method of claim 1 , further comprising:

accessing a subsequently obtained medical image comprising the one or more regions of one or more coronary arteries of a subject;

performing subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia; and

generating a subsequent graphical representation simultaneously displaying results of the analysis of the medical image for atherosclerosis and the analysis of ischemia and the subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia.

12. The computer-implemented method of claim 1 , further comprising:

accessing a subsequently obtained medical image comprising the one or more regions of one or more coronary arteries of a subject;

performing subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia; and

generating a subsequent graphical representation displaying a comparison of the results of the analysis of the medical image for atherosclerosis and the analysis of ischemia and the subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia.

13. The computer-implemented method of claim 1 , wherein the one or more quantified plaque parameters are generated based at least in part on density of the one or more regions of plaque.

14. The computer-implemented method of claim 13 , wherein the density comprises material density.

15. The computer-implemented method of claim 1 , wherein the density comprises radiodensity.

16. The computer-implemented method of claim 15 , wherein low density non-calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about −189 and about 30 Hounsfield units, wherein non-calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about −189 and about 350 Hounsfield units, and wherein calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about 350 and about 2500 Hounsfield units.

17. The computer-implemented method of claim 1 , wherein the analysis of the medical image for atherosclerosis is performed using a machine learning algorithm.

18. The computer-implemented method of claim 1 , wherein the medical image is obtained using coronary computed tomography angiography (CCTA).

19. The computer-implemented method of claim 1 , wherein the medical image is obtained using computed tomography (CT).

20. The computer-implemented method of claim 1 , wherein the medical image is obtained using an imaging modality comprising one or more of CT, x-ray, ultrasound, echocardiography, MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

21. A system for assessing need for coronary revascularization for a subject based on image analysis of a non-invasive medical image of the subject and analysis of one or more plaque parameters or vascular parameters derived from the image analysis, the system comprising:

one or more computer readable storage devices configured to store a plurality of computer executable instructions; and

one or more hardware computer processors in communication with the one or more computer readable storage devices and configured to execute the plurality of computer executable instructions in order to cause the system to:

access a medical image comprising one or more regions of one or more coronary arteries of a subject, the medical image obtained non-invasively;

analyze the medical image to identify stenosis or luminal narrowing present in the one or more regions of the one or more coronary arteries of the subject, wherein the stenosis or luminal narrowing is identified based at least in part on a lumen of the one or more regions of the one or more coronary arteries, wherein the identified stenosis or luminal narrowing is configured to be used to determine a risk of coronary artery disease for the subject (CAD);

perform further analysis of the medical image for atherosclerosis and ischemia when the subject is suspected to have CAD,

wherein analysis for atherosclerosis comprises:

identifying one or more regions of plaque in the one or more regions of the one or more coronary arteries of the subject; and

generating one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque, the one or more quantified plaque parameters comprising one or more of total plaque volume, volume of low-density non-calcified plaque, volume of non-calcified plaque, or volume of calcified plaque, and

wherein analysis for ischemia comprises:

generating one or more vascular parameters of the one or more regions of the one or more coronary arteries, the one or more vascular parameters comprising one or more of vascular morphology, lumen wall, or vessel wall; and

determining a risk of ischemia for the one or more regions of the one or more coronary arteries based at least in part on the generated one or more vascular parameters; and

generating a graphical representation of the analysis for atherosclerosis and the analysis of ischemia for the one or more regions of the one or more coronary arteries, wherein the graphical representation is configured to be used to facilitate determination of a need for coronary revascularization for the subject.

22. The system of claim 21 , wherein the graphical representation comprises a cross-sectional view, a multiplanar view, and a three-dimensional artery view of the one or more regions of the one or more coronary arteries.

23. The system of claim 22 , wherein the cross-sectional view, the multiplanar view, and the three-dimensional artery tree view of the one or more regions of the one or more coronary arteries are configured to be viewed simultaneously.

24. The system of claim 21 , wherein the subject is suspected to have CAD based at least in part on the risk of CAD determined based on the identified stenosis or luminal narrowing.

25. The system of claim 21 , wherein analyzing the medical image to identify stenosis or luminal narrowing present in the one or more regions of the one or more coronary arteries of the subject comprises identifying a sum of all stenoses or luminal narrowings present across a length of a vessel.

26. The system of claim 21 , wherein the risk of ischemia is further determined based at least in part on the one or more quantified plaque parameters.

27. The system of claim 21 , wherein the system is further caused to:

access a subsequently obtained medical image comprising the one or more regions of one or more coronary arteries of a subject;

perform subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia; and

generate a subsequent graphical representation simultaneously displaying results of the analysis of the medical image for atherosclerosis and the analysis of ischemia and the subsequent analysis of the subsequently obtained medical image for atherosclerosis and ischemia.

28. The system of claim 21 , wherein the one or more quantified plaque parameters are generated based at least in part on radiodensity of the one or more regions of plaque on the medical image.

29. The system of claim 21 , wherein the analysis of the medical image for atherosclerosis is performed using a machine learning algorithm.

30. The system of claim 21 , wherein the medical image is obtained using coronary computed tomography angiography (CCTA).

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: MIN, JAMES K; EARLS, JAMES P; RODRIGUES MARQUES, HUGO MIGUEL
To: CLEERLY, INC.
Reel/Frame 062583/0130 →
Continuity (6)
Continuation 17654883 · Mar 15, 2022
Continuation 17393881 · Aug 4, 2021
Continuation 17213966 · Mar 26, 2021
Continuation 17142120 · Jan 5, 2021
Provisional Application 62958032 · Jan 7, 2020
Related Publication 20230137934A1 · May 4, 2023
Cited By (17)
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