Al Hinai Ghalib, Jammoul Samer, Vajihi Zara, Afilalo Jonathan
Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada.
Department of Emergency Medicine, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, H-126, Montreal, QC H3T 1E2, Canada.
Eur Heart J Digit Health. 2021 Aug 7;2(3):416-423. doi: 10.1093/ehjdh/ztab048. eCollection 2021 Sep.
The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular (LV) systolic dysfunction, myocardial hypertrophy, and ischaemic heart disease. A systematic literature search was conducted to identify published original articles on end-to-end DL analysis of resting ECG signals for the detection of structural cardiac pathologies. Studies were excluded if the ECG was acquired by ambulatory, stress, intracardiac, or implantable devices, and if the pathology of interest was arrhythmic in nature. After duplicate reviewers screened search results, 12 articles met the inclusion criteria and were included. Three articles used DL to detect LV systolic dysfunction, achieving an area under the curve (AUC) of 0.89-0.93 and an accuracy of 98%. One study used DL to detect LV hypertrophy, achieving an AUC of 0.87 and an accuracy of 87%. Six articles used DL to detect acute myocardial infarction, achieving an AUC of 0.88-1.00 and an accuracy of 83-99.9%. Two articles used DL to detect stable ischaemic heart disease, achieving an accuracy of 95-99.9%. Deep learning models, particularly those that used convolutional neural networks, outperformed rules-based models and other machine learning models. Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies, which has clinical applicability for more effective screening of asymptomatic populations and expedited diagnostic work-up of symptomatic patients at risk for cardiovascular disease.
本综述的目的是评估深度学习（DL）分析静息心电图（ECG）以预测心脏结构病变（如左心室（LV）收缩功能障碍、心肌肥厚和缺血性心脏病）的证据。我们进行了系统的文献检索，以确定已发表的关于静息ECG信号的端到端DL分析以检测心脏结构病变的原始文章。如果ECG是通过动态、应激、心内或植入式设备获取的，以及如果感兴趣的病变本质上是心律失常性的，则排除相关研究。在重复评审人员筛选检索结果后，有12篇文章符合纳入标准并被纳入。3篇文章使用DL检测LV收缩功能障碍，曲线下面积（AUC）为0.89 - 0.93，准确率为98%。1项研究使用DL检测LV肥厚，AUC为0.87，准确率为87%。6篇文章使用DL检测急性心肌梗死，AUC为0.88 - 1.00，准确率为83 - 99.9%。2篇文章使用DL检测稳定型缺血性心脏病，准确率为95 - 99.9%。深度学习模型，特别是那些使用卷积神经网络的模型，优于基于规则的模型和其他机器学习模型。深度学习是一种很有前景的技术，可用于分析静息ECG信号以检测心脏结构病变，对于更有效地筛查无症状人群以及加快对有心血管疾病风险的有症状患者的诊断检查具有临床适用性。
