Arafati Arghavan, Hu Peng, Finn J Paul, Rickers Carsten, Cheng Andrew L, Jafarkhani Hamid, Kheradvar Arash
The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, USA.
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Cardiovasc Diagn Ther. 2019 Oct;9(Suppl 2):S310-S325. doi: 10.21037/cdt.2019.06.09.
Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies.
心脏磁共振成像（CMR）能够对先天性心脏病（CHD）患者的心脏功能和解剖结构进行无创、非电离评估。在过去十年中，CMR作为一种用于评估CHD的无创成像工具，其效用呈指数级增长。基于人工智能（AI），特别是深度学习的算法，已迅速成为分析CMR的首选方法。为应对CMR各个方面的挑战，人们开发了广泛的AI应用，并且从图像采集到图像分析与诊断也取得了重大进展。我们概述了AI的定义、不同架构以及知名方法的详细信息。本文回顾了用于分析CHD患者的主要深度学习概念。最后，我们总结了一系列未来研究需考虑的开放性挑战和问题。
