STTR Phase I: Feasibility Study of a Reduced Order Model for Calculating Fractional Flow Reserve (FFR) Using Angiographic Data

该小企业技术转让 (STTR) 第一阶段项目的更广泛影响是改善冠状动脉疾病 (CAD) 患者的临床护理并降低相关成本。 CAD 仍然是最常见的心脏病，影响着超过 1800 万成年人，每年给美国医疗保健系统造成的损失超过 900 亿美元。用于改善治疗决策的诊断技术的进步落后于治疗方法的进步。拟议的研究将在人工智能（AI）软件平台中探索新颖的计算建模方法，以改善诊断并优化患者特定的治疗决策。价值主张是改善临床结果、降低医疗成本和拯救生命。该软件作为一种更加用户友好和非侵入性的诊断方法，将提供竞争优势，能够比现有替代方案更快、更准确地对 CAD 进行临床评估。该小企业技术转让 (STTR) 一期项目将推进冠状动脉疾病 (CAD) 的诊断，以优化针对患者的治疗决策。 CAD 患者通常会接受血管造影手术，通过对冠状动脉进行可视化来估计狭窄的严重程度，并就是否进行血运重建手术（例如支架置入术或旁路移植手术）做出主观治疗决定。最近，血流储备分数 (FFR)（一种衡量血管狭窄处压力梯度的指标）在指导治疗决策时已显示出改善的结果。然而，鉴于现有介入设备和成本的缺点，FFR 的采用仍然有限。最近，商业努力开发了基于计算机的方法来非侵入性地估计 FFR，尽管 FFR 边界值的准确性有限。拟议的研究将使用新颖的机器学习和计算方法开发和校准降阶模型（ROM），以比现有解决方案更快、更准确、与临床工作流程更集成的方式提供 FFR 测量。 ROM 将依靠图论的最新进展来增强血流的一维非线性公式。该方法将使用地面实况 3D Navier-Stokes 解决方案生成的合成数据进行校准，并根据 20 名患者的 FFR 临床测量结果进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

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.