Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking
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, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, 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.
1. A computer-implemented method for determining patient-specific coronary artery disease (CAD) risk factor goals based on quantification of coronary atherosclerosis and vascular morphology features using non-invasive medical image analysis, the method comprising:
accessing, by a computer system, a CAD risk factor level for a subject;
accessing, by the computer system, a medical image of the subject, the medical image comprising one or more coronary arteries;
analyzing, by the computer system, the medical image of the subject to perform quantitative phenotyping of atherosclerosis and vascular morphology, the quantitative phenotyping of atherosclerosis comprising analysis of one or more of plaque volume, plaque composition, or plaque progression;
determining, by the computer system, correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis and vascular morphology;
determining, by the computer system, an individualized CAD risk factor level threshold of elevated risk of CAD for the subject based at least in part on the CAD risk factor level and the determined correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis and vascular morphology; and
determining, by the computer system, a subject-specific goal for the CAD risk factor level based at least in part on the determined individualized CAD risk factor level threshold of elevated risk of CAD for the subject, wherein the determined subject-specific goal for the CAD risk factor level is configured to be used to determine an individualized treatment 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 CAD risk factor level comprises one or more of cholesterol level, low-density lipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL) cholesterol level, cholesterol particle size and fluffiness, inflammation level, glycosylated hemoglobin, or blood pressure.
3. The computer-implemented method of claim 1 , wherein the quantitative phenotyping of atherosclerosis is performed based at least in part on analysis of density values of one or more pixels of the medical image corresponding to plaque.
4. The computer-implemented method of claim 1 , wherein the plaque volume comprises one or more of total plaque volume, calcified plaque volume, non-calcified plaque volume, or low-density non-calcified plaque volume.
5. The computer-implemented method of claim 1 , wherein the density values comprise radiodensity values.
6. The computer-implemented method of claim 1 , wherein the plaque composition comprises composition of one or more of calcified plaque, non-calcified plaque, or low-density non-calcified plaque.
7. The computer-implemented method of claim 6 , wherein one or more of the calcified plaque, non-calcified plaque, of low-density non-calcified plaque is identified based at least in part on radiodensity values of one or more pixels of the medical image corresponding to plaque.
8. The computer-implemented method of claim 7 , wherein calcified plaque comprises one or more pixels of the medical image with radiodensity values of between about 351 and about 2500 Hounsfield units, non-calcified plaque comprises one or more pixels of the medical image with radiodensity values of between about 31 and about 250 Hounsfield units, and low-density non-calcified plaque comprises one or more pixels of the medical image with radiodensity values of between about −189 and about 30 Hounsfield units.
9. The computer-implemented method of claim 1 , wherein the plaque progression is determined by: accessing, by the computer system, one or more serial medical images of the patient, the one or more serial medical images comprising one or more coronary arteries; and analyzing, by the computer system, the one or more serial medical images of the patient to determine plaque progression based at least in part on a serial change in plaque volume.
10. The computer-implemented method of claim 9 , wherein the serial change in plaque volume is based on one or more of total plaque volume, calcified plaque volume, non-calcified plaque volume, or low-density non-calcified plaque volume.
11. The computer-implemented method of any one of claim 1 , wherein the vascular morphology comprises one or more of absolute minimum lumen diameter or area, lumen diameter, cross-sectional lumen area, vessel volume, lumen volume, arterial remodeling, vessel or lumen geometry, or vessel or lumen curvature.
12. The computer-implemented method of claim 1 , wherein the correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis is determined based at least in part by multivariable regression analysis.
13. The computer-implemented method of claim 1 , wherein the correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis is determined based at least in part by a machine learning algorithm.
14. The computer-implemented method of claim 1 , wherein the medical image comprises a Computed Tomography (CT) image.
15. The computer-implemented method of claim 1 , wherein the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), 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).
16. The computer-implemented method of claim 1 , wherein the treatment for cardiovascular disease comprises medical intervention, medical treatment, or lifestyle interventions, including but not limited to changes in diet, physical activity, anxiety and stress level, sleep and others.
17. The computer-implemented method of claim 1 , further comprising: accessing, by the computer system, a second medical image of the subject, the second medical image obtained at a later point in time than the medical image; analyzing, by the computer system, the second medical image of the subject to perform quantitative phenotyping of atherosclerosis; recalibrating, by the computer system, the individualized CAD risk factor level threshold of elevated risk of CAD for the subject based at least in part on the quantitative phenotyping of atherosclerosis of the second medical image; and updating, by the computer system, the subject-specific goal for the CAD risk factor level based at least in part on the recalibrated individualized CAD risk factor level threshold of elevated risk of CAD for the subject, wherein the updated subject-specific goal for the CAD risk factor level is configured to used to change or maintain the individualized treatment for the subject.
18. A system for determining patient-specific coronary artery disease (CAD) risk factor goals based on quantification of coronary atherosclerosis using non-invasive medical 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 CAD risk factor level for a subject; access a medical image of the subject, the medical image comprising one or more coronary arteries;
analyze the medical image of the subject to perform quantitative phenotyping of atherosclerosis, the quantitative phenotyping of atherosclerosis comprising analysis of one or more of plaque volume, plaque composition, or plaque progression;
determine correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis; determine an individualized CAD risk factor level threshold of elevated risk of CAD for the subject based at least in part on the CAD risk factor level and the determined correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis; and
determine a subject-specific goal for the CAD risk factor level based at least in part on the determined individualized CAD risk factor level threshold of elevated risk of CAD for the subject, wherein the determined subject-specific goal for the CAD risk factor level is configured to be used to determine an individualized treatment for the subject.
19. The system of claim 18 , wherein the CAD risk factor level comprises one or more of cholesterol level, low-density lipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL) cholesterol level, cholesterol particle size and fluffiness, inflammation level, glycosylated hemoglobin, or blood pressure.
20. The system of claim 18 , wherein the quantitative phenotyping of atherosclerosis is performed based at least in part on analysis of density values of one or more pixels of the medical image corresponding to plaque.
21. The system of claim 18 , wherein the density values comprises radiodensity values.
22. The system of claim 18 , wherein the plaque volume comprises one or more of total plaque volume, calcified plaque volume, non-calcified plaque volume, or low-density non-calcified plaque volume.
23. The system of claim 21 , wherein the plaque composition comprises composition of one or more of calcified plaque, non-calcified plaque, or low-density non-calcified plaque.
24. The system of claim 21 , wherein the plaque progression is determined by: accessing, by the computer system, one or more serial medical images of the patient, the one or more serial medical images comprising one or more coronary arteries; and analyzing, by the computer system, the one or more serial medical images of the patient to determine plaque progression based at least in part on a serial change in plaque volume.
25. The system of claim 21 , wherein the serial change in plaque volume is based on one or more of total plaque volume, calcified plaque volume, non-calcified plaque volume, or low-density non-calcified plaque volume.
26. The system of claim 18 , wherein the correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis is determined based at least in part by multivariable regression analysis.
27. The system of claim 18 , wherein the correlation of the CAD risk factor level with the quantitative phenotyping of atherosclerosis is determined based at least in part by a machine learning algorithm.
28. The system of claim 18 , wherein the medical image comprises a Computed Tomography (CT) image.
29. The system of claim 18 , wherein the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), 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).
30. The system of claim 18 , wherein the treatment for cardiovascular disease comprises medical intervention, medical treatment, or lifestyle change.
31. The system of claim 18 , wherein the system is further caused to: access a second medical image of the subject, the second medical image obtained at a later point in time than the medical image; analyze the second medical image of the subject to perform quantitative phenotyping of atherosclerosis; recalibrate the individualized CAD risk factor level threshold of elevated risk of CAD for the subject based at least in part on the quantitative phenotyping of atherosclerosis of the second medical image; and update the subject-specific goal for the CAD risk factor level based at least in part on the recalibrated individualized CAD risk factor level threshold of elevated risk of CAD for the subject, wherein the updated subject-specific goal for the CAD risk factor level is configured to used to change or maintain the individualized treatment for the subject.
32. The system of claim 18 , wherein the system is further caused to analyze the medical image of the subject to perform phenotyping of vascular morphology, wherein the subject-specific goal for the CAD risk factor level is further determined based at least in part on the phenotyping of vascular morphology, the vascular morphology comprising one or more of absolute minimum lumen diameter or area, lumen diameter, cross-sectional lumen area, vessel volume, lumen volume, arterial remodeling, vessel or lumen geometry, or wherein the subject-specific goal for the CAD risk factor level is further determined based at least in part on the phenotyping of vascular morphology, the vascular morphology comprising one or more of absolute minimum lumen diameter or area, lumen diameter, cross-sectional lumen area, vessel volume, lumen volume, arterial remodeling, vessel or lumen geometry, or vessel or lumen curvature.