人工智能在基于心电图的诊断下左心室射出小部分:一个系统的审查和元分析
Gayathiri R R1, Arya Bhardwaj1, R Pradeep Kumar2
1Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.
Biomedical engineering letters
|July 8, 2025
概括
人工智能 (AI) 增强了心电图 (ECG) 对左心室缩功能障碍 (LVSD) 的检测. AI-ECG模型显示严重的LVSD的高精度,但外部验证是更广泛应用的关键.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 检测左心室缩功能障碍 (LVSD) 对于管理心力衰竭至关重要.
- 电心电图 (ECG) 是标准的,但往往缺乏足够的独立准确性,用于LVSD.
- 人工智能 (AI) 与心电图的整合提供了更好的诊断精度.
研究的目的:
- 系统地审查和元分析用于检测LVSD的AI支持心电图模型的诊断性能.
- 评估射出分数 (EF) 值对AI-ECG模型准确性的影响.
- 通过外部验证来评估AI-ECG工具的稳定性和通用性.
主要方法:
- 对评估AI-ECG用于LVSD检测的研究进行系统审查.
- 17项研究的元分析,使用QUADAS-2进行质量评估.
- 单变量,双变量元分析和斯皮尔曼相关性来评估性能和偏差.
主要成果:
- 对AI-ECG模型的聚合灵敏度和特异性分别为86.9%和84.4%.
- 观察到的最高灵敏度是35%的EF值;50%的EF值显示性能较低.
- 对外部验证的研究有强烈的正相关性 (ρ=0.696),表明了强度.
结论:
- 人工智能支持的心电图显示了诊断严重的LVSD的巨大潜力.
- 目前的AI-ECG模型在检测轻度LVSD方面存在局限性,并且需要外部验证以实现通用性.
- 未来的研究应该专注于改善轻度LVSD检测和减轻出版偏见.
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