用人工智能启用心电图检测大动脉狭窄的外部评估
Darae Kim1, Eunjung Lee2, Jihoon Kim1
1Division of Cardiology, Department of Internal Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
一种人工智能启用的心电图 (AI-ECG) 算法显示,它有望在亚洲患者中检测中度至严重的大动脉狭窄 (AS). 这种AI-ECG工具是在美国开发的,在韩国队列中表现相似,这表明它具有广泛的适用性.
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:
- 大动脉狭窄 (AS) 是一种显著的膜性心脏病.
- 早期发现AS对于及时干预至关重要.
- 基于人工智能的心电图 (AI-ECG) 算法为非侵入性查提供了潜力.
研究的目的:
- 评估美国开发的AI-ECG算法在亚洲患者队列中识别中度至重度AS的性能.
- 为了评估算法的准确性和可靠性,在现实世界的临床环境中,在其原始验证人群之外.
主要方法:
- 对60岁以上的患者进行回顾性分析,这些患者在31天内接受了心声和心电图.
- 不包括之前接受过心脏手术,假发门或心脏起器的患者.
- 应用预先训练的AI-ECG模型 (由梅奥诊所开发),不进行微调,以预测中度至重度的AS,并将结果与TTE确认的诊断进行比较.
主要成果:
- AI-ECG模型实现了0.85的曲线下面积 (AUC) (95%CI:0.84-0.87),用于检测中度至严重的AS.
- 关键性能指标包括灵敏度为0.83,特异性为0.65,正预测值 (PPV) 为0.37,负预测值 (NPV) 为0.94,准确率为68.29%.
- 性能在各个年龄和性别子组中保持一致,老年患者的敏感性得到改善.
结论:
- 与最初的验证相比,AI-ECG算法在亚洲队列中检测中度至重度AS方面表现相似.
- 研究结果支持AI-ECG作为一种有价值的非侵入性查工具,在不同人群中对AS进行查.
- 在更广泛的种族群体中进一步验证可能会提高AI-ECG对AS查的全球适用性.
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