The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to improve the clinical care of patients suffering from coronary artery disease (CAD) and to reduce the associated costs. CAD remains the most common form of heart disease, afflicting more than 18 million adults and costing the U.S. healthcare system over $90 billion annually. Advances in diagnostics to improve treatment decisions have lagged behind advances in therapies. The proposed research will explore novel computational modeling methods within an artificial intelligence (AI) software platform to improve diagnosis and optimize patient-specific treatment decisions. Value propositions are to improve clinical outcomes, reduce healthcare costs, and save lives. The software will provide competitive advantages as a more user-friendly and non-invasive diagnostic method capable of faster and more accurate clinical assessment of CAD than existing alternatives. This Small Business Technology Transfer (STTR) Phase I project will advance the diagnosis of coronary artery disease (CAD) to optimize patient-specific treatment decisions. CAD patients typically undergo an angiography procedure whereby coronary arteries are visualized to estimate stenosis severity and make subjective treatment decisions on whether to perform revascularization procedures (e.g., stenting or bypass graft surgeries). More recently Fractional Flow Reserve (FFR), a measure of the pressure gradient across the vessel stenosis, has demonstrated improved outcomes when guiding treatment decisions. However, adoption of FFR remains modest given shortcomings of available interventional devices and cost. Recently, commercial efforts have developed computer-based methods to estimate FFR non-invasively, although with limited accuracy in borderline values of FFR. The proposed research will develop and calibrate a Reduced Order Model (ROM) using novel machine learning and computational methods to provide FFR measures in a faster, more accurate, and more integrated manner with clinical workflows than existing solutions. The ROM will rely on recent advances in Graph Theory to enhance a 1D nonlinear formulation of blood flow. The method will be calibrated using synthetic data generated with ground truth 3D Navier-Stokes solutions and validated against clinical measurements of FFR from a cohort of 20 patients.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
