Sivanandarajah Pavidra, Wu Huiyi, Bajaj Nikesh, Khan Sadia, Ng Fu Siong
National Heart and Lung Institute, Imperial College London, London, United Kingdom.
Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.
Cardiovasc Digit Health J. 2022 May 16;3(3):136-145. doi: 10.1016/j.cvdhj.2022.04.001. eCollection 2022 Jun.
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
心房颤动（AF）是最常见的心律失常，会导致严重的发病率和死亡率。早期识别房颤可能会带来房颤的早期治疗，从而预防与房颤相关的中风和并发症。然而，目前尚无针对人群进行房颤筛查的正式且具有成本效益的策略。在本综述中，我们简要概述了房颤的靶向筛查、用于筛查的房颤风险评分模型，并描述了不同的筛查工具。接着，我们将广泛讨论机器学习在房颤筛查中的潜在应用。
