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使用多任务深度学习的心肌痕和左心室喷射小部分对心电图像的分类
Atirut Boribalburephan1,2, Sukrit Treewaree3, Noppawat Tantisiriwat3
1Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
Scientific reports
|March 30, 2024
概括
分析二维心电图 (ECG) 图像的深度学习模型可以准确预测心肌痕 (MS) 和左下心室喷射率 (LVEF <50%). 这种具有成本效益的计算机视觉方法为心脏磁共振成像 (CMR) 提供了可行的替代方案.
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:
- 心肌痕 (MS) 和左心室喷射率 (LVEF) 对于心血管评估至关重要.
- 心脏磁共振 (CMR) 是MS和LVEF评估的标准,但在许多地区是昂贵和难以获得的.
- 电心电图 (ECG) 为心血管诊断提供了一个具有成本效益的替代方案.
研究的目的:
- 开发和评估深度学习模型来预测MS和LVEF<50%,使用12导电心电图二维图像.
- 为了进行这些预测,比较2D心电图像分析的性能与1D信号分析.
- 与CMR相比,评估基于ECG的AI作为选工具的潜力.
主要方法:
- 设计了一个多任务深度学习框架,用于分析14052个12-lead ECG 2D图像.
- 对于MS和LVEF的基础真相标签是从CMR获得的.
- 模型的表现使用曲线下面积 (AUC) 度量来评估,并与心脏病学家的表现进行比较.
主要成果:
- 性能最好的模型在MS预测方面达到0.838的AUC,在LVEF<50%分类方面达到0.939.
- 在这些预测中,模型的性能超过了人类心脏病学家的预测.
- 对1D心电图信号的分析显示,与基于2D图像的方法相比,结果较差.
- 一个患病率特定的测试数据集为MS预测产生了0.812的AUC.
结论:
- 基于计算机视觉的深度学习模型可以有效地从心电图扫描图像中分类MS和LVEF<50%.
- 这种人工智能驱动的方法为临床查提供了CMR的成本效益和可访问的替代方案.
- 对于这些心血管参数,2D图像分析方法在1D信号提取上表现出优越的性能.
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