Development of Deep Learning Tools for Widely-Available, Inexpensive Diagnosis of Coronary Microvascular and Vasomotor Dysfunction

Heart disease continues to be a leading cause of disability and death worldwide. Recent research has refined our understanding of the many causes of heart disease and provided many new therapies to treat heart disease. Coronary microvascular and vasomotor dysfunction {CMVD) is a condition that results from aging, diabetes, obesity, hypertension, smoking, and other cardiovascular risk factors and is a common cause of chest pain. Many studies have established that CMVD is associated with adverse cardiac outcomes, including death. Indeed, CMVD is likely to be a causal contributor to epicardial coronary artery disease and vulnerable atherosclerotic plaques through decreased wall shear stress. Despite this, standard testing modalities are unable to consistenUy diagnose CMVD. Generally, CMVD is diagnosed with either expensive, specialized noninvasive stress imaging modalities or even more costly invasive angiographic measurements performed with intracoronary probes. As a result most CMVD diagnoses are presumptive-made after ruling out all other possibilities. This is a cosUy approach with uncertainty for both patients and treating clinicians. Furthermore, difficulty in establishing confirmed diagnoses of CMVD has been a critical impediment to the development of novel therapeutic approaches to CMVD. Consequently, there is a major unmet clinical need for a simple, readily available, and inexpensive diagnostic test for CMVD. The primary aims of this project will be to develop and validate artificial intelligence methods and computer software tools for noninvasive diagnosis and characterization of CMVD. Artificial intelligence methods are able to identify subtle features in complex data that are sometimes difficult or time-consuming for physicians to see. The long-term objective is to develop and commercialize such tools to facilitate improved management of patients with myocardial ischemia without obstructive coronary artery disease, to guide and monitor treatment of such patients, and to provide tools to enable the development of novel therapeutic approaches for CMVD. Project Number: 1R44HL178315-01 | Fiscal Year: 2025 | NIH Institute/Center: National Heart Lung and Blood Institute (NHLBI) | Principal Investigator: Jonathan Moody | Institution: INVIA, LLC, ANN ARBOR, MI | Award Amount: $1,107,749 | Activity Code: R44 | Study Section: Special Emphasis Panel[ZRG1 IVBH-V (11)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R44HL17831501