在急诊室使用人工智能应用于心电图的胸痛风险分层
Julian S Haimovich1,2, Márton Kolossváry3,4,5, Ridwan Alam1,6
1Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Open heart
|September 1, 2025
概括
人工智能可以改善胸痛诊断. 胸部疼痛-AI是一个人工智能工具,可以准确地预测急诊室患者在7天内发生的主要心血管事件.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- 在急诊室的标准化胸痛评估面临主观评估和不一致的诊断测试的挑战.
- 这些局限性导致医疗费用增加,医疗差异和患者的不良结果.
- 人工智能为胸痛患者的风险分层过程提供了一个有前途的途径.
研究的目的:
- 开发和验证基于AI的分类器,胸痛-AI (CP-AI),用于在ED出现后7天内预测主要的心血管诊断.
- 将CP-AI与传统风险分层模型的性能进行比较.
- 评估CP-AI在标准化和加快急性胸痛患者风险分层方面的潜力.
主要方法:
- 为了训练神经网络分类器 (CP- AI),使用了15,048名ED患者的追溯队列.
- CP-AI使用了患者的人口统计数据,心脏生物标志物 (D-二次元,热素) 和深度学习衍生的心电图 (ECG) 表示.
- 在对14476名患者的外部验证组中,使用受体操作特征曲线下的区域 (AUROC) 和精度回忆曲线下的区域 (AUPRC) 评估性能,将CP- AI与生物标志物模型进行比较.
主要成果:
- CP- AI的AUROC为0.82 (95% CI为0.81- 0.83) 和AUPRC为0.46 (95% CI为0.44- 0.49).
- 生物标志物模型的AUROC值为0. 79 (95% CI为0. 78- 0. 81) 和AUPRC值为0. 35 (95% CI为0. 33- 0. 37).
- 与生物标志物模型相比,CP- AI在预测主要心血管诊断和全因死亡率的7天复合终点方面表现出统计学上显著的优势 (p<0. 05).
结论:
- 自动神经网络分类器CP-AI在预测急性胸痛患者7天内主要心血管诊断方面的表现明显优于传统模型.
- CP-AI显示了在紧急部门标准化和加快风险分层过程的潜力.
- 这种人工智能工具可能有助于缓解诊断不一致性,并改善急性胸痛患者的治疗结果.
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