基于心电图的诊断和预测模型用于快速临床应用
Md Saiful Islam1, Sunil Vasu Kalmady2, Abram Hindle3
1Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
人工智能 (AI) 在心电图 (ECG) 分析中显示出对诊断心脏和非心脏疾病的前景. 然而,大多数研究都是回顾性的,强调了前性验证的必要性,并解决了广泛临床采用的挑战.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 人工智能 (AI) 越来越多地用于心电图 (ECG) 分析,为改善心血管和非心脏疾病的诊断和预后提供了潜力.
- 在过去的五年中,人工智能驱动的ECG应用取得了重大进展,这得益于深度学习和ECG技术的更广泛采用.
研究的目的:
- 从2019-2023年开始,审查临床研究和AI增强的心血管疾病检测,诊断和预后的ECG应用.
- 确定挑战,并提出解决方案,以有效的临床集成和全球可扩展性在心电图分析AI.
主要方法:
- 2019年至2023年期间发表的AI增强型心电图分析临床研究的系统审查.
- 对AI-ECG应用程序开发,验证,监管批准和临床实施的分析.
主要成果:
- 大多数发表的研究是单一中心,回顾性,概念验证调查,缺乏外部验证.
- 适合临床部署的前性研究占研究的不到15%.
- 经FDA批准的AI-ECG应用程序通常来自商业合作,其中许多针对移动或可穿戴设备.
结论:
- 在心电图分析中,人工智能领域还处于芽阶段,面临诸多障碍,包括未来的多中心验证,技术问题,偏见,数据安全性和通用性.
- 克服这些挑战对于开发用于心血管疾病管理的全面,临床集成和全球可扩展的人工智能解决方案至关重要.
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