人工智能检测到无症状心房动和发生缺血性中风和心血管事件的风险:英国生物库研究
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
人工智能 (AI) 在心电图中检测到无症状心房动 (AF) 信号增加了中风和主要不良心血管事件 (MACE) 的风险. 这种人工智能方法可以在临床诊断之前识别个人进行更早的预防性干预.
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:
- 可穿戴设备和人工智能驱动的心电图分析越来越多地在临床环境之外检测出无症状心房动 (AF).
- 人工智能检测到无症状AF对心血管风险的预后影响尚不清楚.
- 有限的证据指导AI检测无症状AF的个体的风险分层.
研究的目的:
- 为了调查AI检测的无症状AF和发生心血管结果之间的关联.
- 通过AI-ECG分析,通过AI-ECG分析确定无症状AF的个体来评估心血管风险.
- 探索AI-ECG的潜力,以早期识别面临AF相关并发症风险的个体.
主要方法:
- 一个经过验证的开源深度学习模型 (AI-AF) 应用于来自英国生物库参与者的12个心电图.
- 被AI检测到的AF并且没有先前临床诊断的参与者被归类为无症状AF.
- 卡普兰-梅尔曲线,日志级别测试和调整的考克斯比例危险模型被用于评估6年来缺血性中风和MACE等结果.
主要成果:
- 在6年内,AI检测到无症状AF的参与者表现出明显高的缺血性中风发病率 (1.5%),与没有AF的人 (0.52%) 相比.
- 在使用自由AI-AF值 (危险比1.6) 归类为无症状AF的个体中,MACE的风险高出62%.
- 人工智能检测到无症状的AF确定了缺血性中风,心肌梗塞和心血管死亡的风险增加,结果低于临床诊断的AF,但高于非AF个体.
结论:
- 人工智能检测的无症状AF识别了患缺血性中风和MACE风险增加的个体.
- AI-ECG模型可以检测传统临床评估中遗漏的亚临床AF相关风险.
- 这种人工智能方法可以增强对没有临床诊断AF的个体的早期预防干预.
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