PDF(Pubmed)
Abstract：
Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.
摘要：
应用于心血管疾病（CVD）的人工智能（AI）在科学研究领域取得了巨大的成功。心电图（ECG）是心脏病学检查的基石形式，并且是最广泛使用的诊断工具，因为它们广泛可用，便宜,而且很快。AI在ECG中的应用，特别是使用卷积神经网络(CNN)的深度学习(DL)方法，近年来在心脏病学的许多领域得到了发展。深度学习方法为快速心电图解释提供了有价值的支持，通过对ECG迹线宏观变化的经典分析，证明其诊断能力与CVD诊断专家重叠。通过光电体积描记术，可穿戴设备可以获得用于识别AI诊断的心律失常的单导数ECG。此外,CNNs已经被开发出来，它不能识别宏观的心电图变化，并且可以预测，来自12导联心电图，心房颤动,甚至从窦性心律;左右心室功能;肥厚型心肌病;急性冠状动脉综合征;或主动脉瓣狭窄。应用领域很多,但有很多限制，主要与获取数据的可靠性有关，无法验证黑匣子进程，以及医学法律和道德问题。现代医学的挑战是认识到人工智能的局限性并克服它们。
