计算机断层扫描衍生的左心室质量的自动估计,使用基于性别特异的12心电图的时卷积网络
Heng-Yu Pan1,2, Benny Wei-Yun Hsu3, Chun-Ti Chou3
1Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu City, Taiwan.
European heart journal. Digital health
|January 23, 2026
概括
一种新的深度学习方法,eLVMass-Net,通过ECG准确地估计左心室质量 (LVM). 性别特定的模型改善了左心室缩 (LVH) 的分类,优于现有的方法.
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:
- 左心室质量 (LVM) 是心血管健康的关键指标.
- 准确的LVM估计对于诊断和管理心脏病状况至关重要.
- 目前用于LVM评估的非侵入性方法存在局限性.
研究的目的:
- 引入eLVMass-Net,这是一个深度学习模型,用于使用12个导向ECG估计LVM.
- 根据最先进的方法评估eLVMass-Net的性能.
- 调查性别特异型模型对改善LVM估计和左心室缩 (LVH) 分类的实用性.
主要方法:
- 使用TW-CVAI数据集 (n=1459) 中的原始心电图信号,人口数据和心电图参数开发了eLVMass-Net (n=1459).
- 使用时卷积网络 (TCN) 处理同步的单心跳波形.
- 在NTUH数据集 (n=2579) 上进行外部验证,并与两个SOTA模型进行比较,包括性别特异变化.
主要成果:
- 非性别特定的eLVMass-Net通过五倍交叉验证实现了MAE14.3 ± 0.7g和MAPE12.9 ± 1.1%.
- 在LVM估计和LVH分类方面,eLVMass-Net的表现优于SOTA模型.
- 与非性别特定模型 (0.70) 相比,性别特定模型显示出优异的LVH分类 (c-统计:男性0.77,女性0.75).
结论:
- 使用同步单一心跳提取和TCN的eLVMass-Net,超过了以前基于心电图的LVM估计方法.
- 性别特定模型的开发是提高诊断准确性的合理和有效方法.
- 该模型的突出性地图显示了LVM预测的ST-T部分中性别特异性特征权重.
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