顺序AI-ECG诊断协议用于机会性心房的查:一个回顾性单中心研究
Ji-Hoon Choi1, Sung-Hee Song2, Jongwoo Kim2
1Division of Cardiology, Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Republic of Korea.
Journal of clinical medicine
|September 27, 2025
概括
使用串行心电图的AI心电图查协议改善了心房的检测. 这种两阶段的方法提高了准确性,并优先监测高风险患者,有助于及时的抗凝血.
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:
- 心房动 (AF) 发作往往无症状,延迟了关键的抗凝治疗.
- 目前的查方法可能会错过间歇性AF,影响患者的结果.
- 由于资源有限,需要有效和准确的选工具.
研究的目的:
- 评估一个两阶段的人工智能 (AI) 电心电图 (ECG) 查协议,用于检测心房动 (AF).
- 提高诊断准确度,优化AF查中的资源配置.
- 在初始查后评估串行ECGAI模型的性能.
主要方法:
- 分析了来自164,793名成年人的248,612个12ECG,用于AI模型开发.
- 实施两阶段协议:初始单个ECGAI模型,如果需要,在三个月后再进行串行ECGAI模型.
- 在11,349名符合条件的患者中使用AUROC,灵敏度,特异性,准确性和F1得分等指标进行绩效评估.
主要成果:
- 该协议实现了0.908.8的接收器操作特征曲线 (AUROC) 下的高面积.
- 灵敏度为88.1%,特异性为78.7%,负预测值 (NPV) 为98.4%.
- 该协议正确识别了84.9%的AF阳性患者有中风病史.
结论:
- 一个连续的AI ECG策略有效地保持了高的NPV,并通过纵向确认改善了正预测值 (PPV).
- 这种人工智能驱动的方法可以优先考虑对AF检测最有可能受益的患者的门诊监测.
- 进一步的前性,多中心验证和成本效益研究是有必要的.
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