Papalamprakopoulou Zoi, Stavropoulos Dimitrios, Moustakidis Serafeim, Avgerinos Dimitrios, Efremidis Michael, Kampaktsis Polydoros N
Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, United States.
Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
Front Cardiovasc Med. 2024 Jul 15;11:1432876. doi: 10.3389/fcvm.2024.1432876. eCollection 2024.
Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.
心房颤动（AF）会显著增加中风和心力衰竭的风险，但通常无症状且呈间歇性发作；因此，其及时诊断颇具挑战。在部分患者中早期检测出该病，或许有助于预防中风，并通过及时干预减轻心脏结构并发症。智能手表结合强大的人工智能（AI）算法，因其广泛使用、操作简便且具有潜在成本效益，为早期检测提供了一种很有前景的工具。市面上的智能手表已获得美国食品药品监督管理局（FDA）的许可用于检测房颤，且越来越受欢迎。尽管前景广阔，但基于人工智能的智能手表房颤检测技术不断发展，也引发了对这项技术临床价值的质疑。随着医疗保健领域持续的数字化转型，临床医生应熟悉基于人工智能的智能手表在房颤检测中的工作原理，并明确其在临床环境中的作用，以便提供最佳的患者护理。在本综述中，我们简要概述了基于人工智能的智能手表算法的特点、诊断性能、临床价值、局限性，并探讨房颤诊断的未来前景。
