在怀疑急性冠状动脉综合征时,使用深度学习来进行心电图解释的医院前风险分层化
Jesse P A Demandt1, Thomas P Mast2, Konrad A J van Beek2
1Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands jesse.demandt@catharinaziekenhuis.nl.
BMJ health & care informatics
|June 6, 2025
概括
使用卷积神经网络 (CNN) 的新人工智能 (AI) 模型在紧急医疗服务 (EMS) 中有望识别非ST升高急性冠状动脉综合征 (NSTE-ACS) 的低风险患者. 虽然人工智能改善了心电图的解释,但临床风险得分仍然优越,但可以通过人工智能集成来提高.
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:
- 胸痛是一种常见的紧急医疗服务 (EMS) 呈现,通常被怀疑是非ST升高急性冠状动脉综合征 (NSTE-ACS).
- 仅使用医院前心电图来区分NSTE-ACS与非心脏病原因是具有挑战性的.
- 准确的风险分层对于适当的患者分拣至关重要,减少急诊室过度拥挤和医疗保健成本.
研究的目的:
- 开发和验证基于CNN的模型 (ECG-AI) 用于NSTE-ACS在医院前环境中的风险分层.
- 将ECG-AI的诊断性能与现有的医院前工具进行比较.
- 评估将AI整合到临床风险得分中的潜力,以提高诊断准确度.
主要方法:
- 一个CNN模型 (ECG-AI) 被训练并验证在内部和外部队列的疑似NSTE-ACS患者.
- 在ECG-AI,护理人员ECG解读 (ECG-EMS),临床护理点素和preHEART临床风险评分之间进行了诊断性能比较.
- 该研究包括5645名涉嫌NSTE-ACS的患者,外部验证队列为754名患者.
主要成果:
- 与护理人员ECG解释相比,ECG-AI显示了NSTE-ACS的优越诊断性能 (AUROC 0.70与0.65).
- 在preHEART临床风险得分显示,诊断准确度 (AUROC 0.78) 比单独ECG-AI更高.
- 将ECG-AI集成到preHEART得分中显著改善了诊断性能 (AUROC 0.83).
结论:
- 人工智能在医院前心电图解释中的整合有助于识别低风险的NSTE-ACS患者.
- 目前的临床风险得分提供了最佳的诊断性能,但可以通过AI进一步改进.
- 这些发现支持进一步研究AI在急性冠状动脉综合征的医院前风险分层中的作用.
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