人工智能通过心电图检测心肌梗塞:一个范围审查
Sosana Bdir1, Mennatallah Jaber1, Osaid Tanbouz1
1Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine.
Journal of clinical medicine
|October 16, 2025
概括
人工智能 (AI) 显示出使用心电图 (ECG) 检测心肌梗塞 (MI) 的前景. 然而,目前的AI诊断性能受到数据集和验证问题的限制,需要标准化才能可靠的临床使用.
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:
- 急性心肌梗塞 (MI) 仍然是全球主要的死亡原因,这给医疗保健带来了重大挑战.
- 早期和准确的心脏病发作检测至关重要,但尽管诊断进步,但往往很困难.
- 人工智能 (AI) 正在成为增强基于心电图 (ECG) 的心脏病发作检测的强大工具.
研究的目的:
- 系统地绘制和评估AI在使用心电图数据检测MI中的应用.
- 为AI驱动的MI检测方法提供全面的概述.
- 为了确定基于ECG的MI诊断AI的趋势和挑战.
主要方法:
- 通过心电图进行MI检测的AI应用程序的系统范围审查.
- 从2015年到2024年10月在主要数据库 (MEDLINE,Embase,Web of Science,Cochrane) 中进行的搜索.
- 包括220项遵循PRISMA-ScR指南的研究,提取AI模型,算法,心电图类型和性能指标的数据.
主要成果:
- 自2015年以来,人工智能用于MI检测的应用迅速增加,到2022年达到顶峰.
- 卷积神经网络和支向量机器是占主导地位的AI模型,通常使用12导电心电图.
- 报告的高性能指标通常基于小的,单一来源的数据集,具有乐观的验证,显示有限的概括性和潜在的偏见.
结论:
- 使用心电图的基于AI的MI检测是一个快速增长的领域.
- 目前的诊断性能受到数据集限制和验证方法的显著限制.
- 报告,数据集和验证的标准化对于临床集成,可解释性和AI在MI检测中的公平部署至关重要.
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