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ABSTRACT:

Patient-specific computational models of blood flow in arteries derived from medical imaging data have generated significant interest since they were first introduced more than 25 years ago [1]. Such models enable a new approach in medicine whereby predictive computational models can be used to evaluate and select alternate treatment strategies [2]. Since they were first introduced in 2010, patient-specific models of coronary artery blood flow constructed from coronary CT angiography (cCTA) images leveraging deep learning A.I. methods and using computational fluid dynamics have transformed the diagnosis of heart disease [3]. Such noninvasive, computational models have provided safer, less expensive and more efficient procedures as compared to the standard of care that often involves nuclear imaging and invasive diagnostic cardiac catheterizations. Such image-based computations require an accurate segmentation of the coronary artery lumen from cCTA images and employ biologic principles relating form (anatomy) to function (physiology). HeartFlow developed a non-invasive test, FFR_CT, based on computing flow and pressure in the coronary arteries [3,4]. FFR_CT has been validated against invasive pressure measurements in more than 1000 patients and demonstrated to improve care in over 100 clinical studies enrolling more than 100,000 patients. At present, FFR_CT has been used for routine clinical decision making in more than 1300 hospitals that have served over 400,000 patients in the United States, Europe, and Japan. In the United States, the American College of Cardiology and the American Heart Association guidelines include FFR_CT in the recommended diagnostic pathway for heart disease. Medicare and most U.S. private insurance companies reimburse physicians for using FFR_CT. New products including AI-enabled software for quantifying coronary anatomic narrowings, quantifying coronary atherosclerotic plaque, and predicting changes in blood flow arising from alternate treatment plans will be discussed. Future opportunities for research in developing and applying computational methods for diagnosing and treating cardiovascular and respiratory diseases will be presented.    

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