一个深度学习算法增强的心电图解读,用于检测肺栓塞
Yu-Cheng Chen1, Sung-Chiao Tsai2, Chin Lin3,4,5
1Department of Internal Medicine.
Acta Cardiologica Sinica
|November 29, 2023
概括
使用心电图 (ECG) 的深度学习模型 (DLM) 显示出对诊断肺栓塞 (PE) 的前景. 由DLM错误分类的非PE患者面临更高的死亡率和住院风险.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 肺栓塞 (PE) 的早期诊断具有挑战性.
- 电心电图 (ECG) 和D-二次数水平是当前的查工具.
研究的目的:
- 使用ECG开发一个深度学习模型 (DLM) 进行PE检测.
- 在非PE患者中调查虚假阳性DLM预测的临床意义.
主要方法:
- 在113个PE和51,456个非PEECG上训练了一名DLM.
- 在一个独立的27个PE和13,105个非PE案例中验证了DLM.
- 与使用ROC曲线,灵敏度和特异性的医生比较DLM性能.
主要成果:
- DLM获得了70.8%的灵敏度和69.7%的特异性,与医生相比.
- 通过D-二次元和人口统计数据,DLM性能改善到AUC为0.9.
- 在非PE患者中,错误的DLM预测与所有原因死亡率 (HR 2.13) 和住院风险 (HR 1.55) 的增加相关.
结论:
- 一个DLM增强的ECG系统可以帮助PE识别.
- DLM预测提供了预后见解,特别是在假阳性病例中.
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