Systems, devices, and methods for non-invasive image-based plaque analysis and risk determination
AssigneeCleerly, Inc.
InventorJames K. Min
Life of this patent
Estimating the term…
Abstract
Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.
Claims20
A computer-implemented method of perioperative assessment of risk of postoperative myocardial infarction for a patient based at least in part on automated analysis of one or more medical images, the computer-implemented method comprising:
- accessing, by a computer system, one or more medical images of a patient, the one or more medical images comprising a representation of a portion of one or more coronary arteries;
- analyzing, by the computer system, the one or more medical images to identify one or more coronary arteries, the one or more coronary arteries comprising one or more regions of plaque;
- analyzing, by the computer system, the identified one or more coronary arteries and the one or more regions of plaque to generate a plurality of image-derived variables, the plurality of image-derived variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, or low-density plaque volume;
- and
- applying, by the computer system, a machine learning algorithm to determine risk of postoperative myocardial infarction subsequent to a surgical operation for the patient based at least in part on the plurality of image-derived variables, wherein the machine learning algorithm is trained based at least in part on the plurality of image-derived variables derived from medical images of other subjects with known risk of myocardial infarction, wherein the determined risk of postoperative myocardial infarction is configured to be utilized to determine a need for perioperative treatment or planning for the patient,
- wherein the computer system comprises a computer processor and an electronic storage medium.
A system for determining perioperative assessment of risk of post-operative myocardial infarction for a patient based at least in part on automated analysis of one or more medical images, the system comprising:
- a non-transitory computer storage medium configured to at least store computer executable instructions;
- and
- one or more computer hardware processors in communication with the non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to at least:
- accessing one or more medical images of a patient, the one or more medical images comprising a representation of a portion of one or more coronary arteries;
- analyzing the one or more medical images to identify one or more coronary arteries, the one or more coronary arteries comprising one or more regions of plaque;
- analyzing the identified one or more coronary arteries and the one or more regions of plaque to generate a plurality of image-derived variables, the plurality of image-derived variables comprising one or more of lesion length, remodeling index, plaque slice percentage, stenosis area percentage, presence of low-density plaque, stenosis diameter percentage, presence of positive remodeling, reference diameter after stenosis, reference diameter before stenosis, vessel length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, total plaque volume, number of mild stenosis, or low-density plaque volume;
- and
- applying a machine learning algorithm to determine risk of post-operative myocardial infarction subsequent to a surgical operation for the patient based at least in part on the plurality of image-derived variables, wherein the machine learning algorithm is trained based at least in part on the plurality of image-derived variables derived from medical images of other subjects with known risk of myocardial infarction, wherein the determined risk of post-operative myocardial infarction is configured to be utilized to determine a need for perioperative treatment or planning for the patient.
Dependencies are inferred from the claim text.