Global Health Neurology Lab, Sydney, Australia.
UNSW Medicine and Health, South West Sydney Clinical Campuses, University of New South Wales (UNSW), Sydney, Australia.
Ann N Y Acad Sci. 2024 Nov;1541(1):24-36. doi: 10.1111/nyas.15231. Epub 2024 Oct 8.
Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, including an increased risk of stroke and poor outcomes poststroke. Our understanding of the prognosis in AF remains poor. Machine learning (ML) has been applied to the diagnosis, management, and prognosis of AF in the context of stroke but remains suboptimal for clinical use. This article endeavors to provide a comprehensive overview of current ML applications to AF patients at risk of stroke, as well as poststroke patients without AF. Strategies to develop effective ML involve the validation of a variety of ML algorithms across internal and external datasets as well as exploring their predictive powers in hypothetical and realistic settings. Recent literature of this rapidly evolving field has displayed much promise. However, further testing and innovation of medical artificial intelligence are required before its imminent introduction to ensure complete patient trust within the community. Prioritizing this research is imperative for advancing the optimization of ongoing care for AF patients, as well as the management of stroke patients with AF.
心房颤动（AF）是一种严重的疾病，与高发病率和死亡率相关，包括中风风险增加和中风后预后不良。我们对 AF 的预后了解仍不充分。机器学习（ML）已应用于中风背景下的 AF 的诊断、管理和预后，但仍不适用于临床使用。本文旨在全面概述当前 ML 在有中风风险的 AF 患者以及无 AF 的中风后患者中的应用。开发有效的 ML 策略涉及在内部和外部数据集上验证各种 ML 算法，并在假设和实际环境中探索其预测能力。这一快速发展领域的最新文献显示出了很大的希望。然而，在其即将引入之前，还需要对医疗人工智能进行进一步的测试和创新，以确保在社区内获得患者的完全信任。优先考虑这项研究对于优化 AF 患者的持续护理以及管理有 AF 的中风患者至关重要。
