人工智能在心房颤动研究与临床中的应用
Research and Application of Artificial Intelligence in Atrial Fibrillation
王露 1欧阳微娜 1张璇 1李佳欣 1唐薇 1范咏梅 1肖春霞1
作者信息
- 1. 湖南师范大学附属第一医院(湖南省人民医院)(410005)
- 折叠
摘要
心房颤动作为全球最常见的持续性心律失常,其高发病率和死亡率对公共卫生系统构成了重大经济负担.早期发现房颤对于及时治疗和预防如中风等并发症至关重要.尽管12导联心电图是诊断房颤的金标准,但其依赖于心脏病专家的临床经验,并可能受到观察者间差异的影响.人工智能(AI),尤其是深度学习(DL)中的卷积神经网络(CNN),在图像识别任务中取得了显著进展,能够以更高的准确性和效率自动评估复杂医学图像.本文旨在全面概述人工智能技术,探讨其在房颤疾病研究与临床应用中的潜力.
Abstract
Atrial fibrill ation,as the most common persistent arrhythmia worldwide,poses a significant economic burden to public health systems due to its high morbidity and mortality.Early detection of atrial fibrillation is essential for timely treatment and prevention of complications such as stroke.Although 12-lead ECG is the gold standard for diagnosing atrial fibrillation,it relies on the clinical experience of a cardiologist and may be affected by inter-observer variability.Artificial intelligence(AI),especially convolutional neural networks(CNNs)in deep learning(DL),have made significant progress in image recognition tasks,enabling the automated evaluation of complex medical images with greater accuracy and efficiency.The purpose of this article is to provide a comprehensive overview of AI technology and explore its potential for research and clinical application in atrial fibrillation.
关键词
人工智能/心房颤动/心电图/深度学习/卷积神经网络Key words
Artificial intelligence/Atrial fibrillation/Electrocardiogram/Deep learning/Convolutional Neural Network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据