基于鼻腔节律心电图的隐藏辅助路径的深度学习介导的预测
Lei Wang1,2, Fang Yang1, Xiao-Jing Bao1
1Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.
概括
深度学习有效地检测使用正常心电图 (ECG) 图像隐藏的辅助路径 (AP). 这种方法提供了一种有前途的方法来识别这些无法检测到的心脏病.
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:
- 隐藏的辅助通路 (APs) 可以导致心房心室逆入性心力衰竭.
- 这些AP通常无症状,在正常鼻节律期间无法检测到,这构成了诊断挑战.
研究的目的:
- 开发和评估一种深度学习模型,用于从标准心电图 (ECG) 图像中检测隐藏的AP.
- 评估各种卷积神经网络 (CNN) 对此诊断任务的有效性.
主要方法:
- 收集了隐藏AP患者和健康对照患者的正常鼻节律ECG图像.
- 培训并测试了六个受欢迎的CNN (ResNet26,SE-ResNet50,MobileNetV3_large_100,DenseNet169等等) 的使用情况. 使用ImageNet预训练和随机初始化.
- 使用单独的培训和测试数据集进行模型验证.
主要成果:
- 深度学习模型实现了高诊断性能,特定模型达到>87.0%的灵敏度和>98.0%的特异性.
- 两组之间没有观察到PR和QRS间隔的显著差异,但QT和QTc间隔显示出轻微的变化.
- 从随机初始化中训练的模型表现出与使用ImageNet预训练的模型相似的性能.
结论:
- 深度学习提供了一种有效的策略,用于使用常规ECG预测隐藏的AP.
- 这些发现支持从随机初始化训练深度学习模型的潜力,用于基于心电图的诊断任务.
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