基于深度学习的心电图分析预测了先天性心脏病中的双功能障碍和扩张
Joshua Mayourian1, Addison Gearhart1, William G La Cava2
1Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
Journal of the American College of Cardiology
|August 21, 2024
概括
人工智能增强心电图 (AI-ECG) 分析显示,在先天性心脏病 (CHD) 中检测双心室功能障碍具有前途. 这个AI-ECG模型是为了预测双心室功能障碍和扩张而开发和验证的,有助于临床决策.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能增强心电图 (AI-ECG) 分析显示了识别双心室病理生理学的潜力.
- 然而,AI-ECG分析在先天性心脏病 (CHD) 中的应用需要进一步研究.
研究的目的:
- 开发和外部验证一个AI-ECG模型.
- 该模型旨在预测心血管磁共振 (CMR) 定义的心血管磁共振 (CHD) 患者的双心室功能障碍和扩张.
主要方法:
- 一个卷积神经网络被训练和测试在来自有和没有心脏病的患者的配对ECG-CMR数据上.
- 该模型旨在检测左心室 (LV) 功能障碍,RV功能障碍和LV/RV扩张.
- 在内部测试和外部验证期间,使用接收器操作曲线 (AUROC) 下的面积和精度回忆曲线下的面积来评估性能.
主要成果:
- 该模型在内部测试和外部验证队伍中表现一致.
- 对于LV功能障碍,LV扩张,RV功能障碍和RV扩张的AUROC值在两个队列中都很高.
- 功能单心室患者的表现最低,特定的心电图特征如QRS扩张和T波逆转被确定为VR功能障碍/扩张的高风险指标.
结论:
- AI-ECG分析对预测心脏病患者的双心室功能障碍和扩张具有显著的前景.
- 这项技术可能有助于优化心血管磁共振 (CMR) 成像在心血管疾病管理中的时间.
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