用于心脏CT中冠状动脉疾病评估的机器学习：一项综述
Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey.
作者信息
Hampe Nils, Wolterink Jelmer M, van Velzen Sanne G M, Leiner Tim, Išgum Ivana
机构信息
Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
出版信息
Front Cardiovasc Med. 2019 Nov 26;6:172. doi: 10.3389/fcvm.2019.00172. eCollection 2019.
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
摘要
心脏计算机断层扫描（CT）能够以高空间分辨率快速显示心脏和冠状动脉。然而，分析心脏CT扫描以发现冠状动脉疾病的表现既耗时又具有挑战性。机器学习（ML）方法有潜力以高精度和一致的性能应对这些挑战。在这篇小型综述中，我们对基于机器学习分析心脏CT中冠状动脉疾病的文献进行了综述。我们总结了用于检测和表征动脉粥样硬化斑块以及具有解剖学和功能意义的冠状动脉狭窄的机器学习方法。
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