对心电图波形进行深度学习,以分层阻塞性稳定冠状动脉疾病的风险
Rishi K Trivedi1, I Min Chiu1, John Weston Hughes2
1Department of Cardiology, Cedars-Sinai Medical Center, Smidt Heart Institute, 127 S San Vicente Boulevard #A3600, Los Angeles, CA, USA.
European heart journal. Digital health
|May 21, 2025
概括
结合心电图 (ECG) 数据和临床因素的新型深度学习模型显示,对疑似慢性冠状动脉疾病 (CCD) 患者预测阻塞性冠状动脉疾病 (oCAD) 的准确性有所提高. 这种多模式的方法超越了传统的风险评估工具.
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:
- 冠状动脉疾病 (CAD) 的发病率正在上升,增加了慢性冠状动脉疾病 (CCD) 的负担.
- 目前对阻塞性CAD (oCAD) 的风险评估缺乏足够的诊断准确性.
- 深度学习 (DL) 提供了改善诊断能力的潜力.
研究的目的:
- 开发和验证使用心电图波形和临床特征的DL算法,以预测疑似CCD患者的oCAD.
- 将DL模型的诊断准确性与传统风险评估工具进行比较.
主要方法:
- 使用心电图波形 (DL-ECG),临床特征 (DL-Clinical) 和组合 (DL-MM) 的DLL模型的开发.
- 与CAD联盟 (CAD2) 风险评分进行比较.
- 在外部队列中验证.
主要成果:
- 与DL-临床 (AUC 0.762),DL-心电图 (AUC 0.741) 和CAD2 (AUC 0.733) 相比,DL-MM模型显示出更高的性能 (AUC 0.807).与DL-临床 (AUC 0.762),DL-心电图 (AUC 0.741) 和CAD2 (AUC 0.733) 相比,DL-MM模型显示出更高的性能.
- 外部验证显示DL-MM (AUC 0.716) 和CAD2 (AUC 0.715) 之间的性能可比.
结论:
- 结合心电图和临床数据的多模式DL模型改善了CCD中的oCAD预测.
- 需要进一步的前性研究来评估DL在ECG分析中的临床影响,以确定oCAD诊断和患者的治疗结果.
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