Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

中文翻译：

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人工智能在心电图上的应用

人工智能 (AI) 赋予了心电图 (ECG) 和临床医生读取它们的超人类诊断能力。通过在庞大的数据集中发现通常的亚临床模式，在没有硬编码规则的情况下进行训练，人工智能将心电图（一种无处不在的、非侵入性的心脏测试，集成到实践工作流程中）转变为心脏和非心脏疾病的筛查工具和预测器，通常在无症状个体中。这篇综述描述了受监督的 AI 算法背后的数学背景，并讨论了选定的 AI ECG 心脏筛查算法，包括用于检测左心室功能障碍、从正常窦性心律期间记录的追踪中检测到的发作性心房颤动以及其他结构和瓣膜疾病的算法。从大数据集中学习的能力，无需了解生物学机制，就为检测 COVID-19 等非心脏疾病创造了机会，并带来了数据隐私方面的挑战。与所有医学测试一样，人工智能心电图必须在真实世界的临床环境中经过仔细审查和验证。最后，通过允许从智能手机和可穿戴设备获取医疗级心电图的移动形式因素，人工智能的使用可以实现大规模的可扩展性，使医疗保健民主化。