Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography
Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography
复制标题
DOI：
10.1152/ajpheart.00416.2020
复制
发表时间：
2021-08-01
影响因子：
4.8
通讯作者：
Lee，Alex Pui-Wai
中科院分区：
文献类型：
--
作者：
Liu，Xin;Fan，Yiting;Lee，Alex Pui-Wai
深度学习（DL）已被应用于自动左心室（LV）射血分数（EF）测量，但很少对各种心脏病表型的诊断性能进行评估。本研究旨在使用从三个中心收集的二维超声心动图（2DE）图像评价用于自动LVEF测量的新DL算法。评价了三种超声仪器和三种心脏病表型对自动LVEF测量的影响。我们使用340名患者的36890帧2DE，开发了一种基于U-Net的DL算法（DPS-Net），并应用双平面Simpson方法计算LVEF。结果表明，使用DPS-Net在LV分割和LVEF测量中具有很高的性能，并且可以跨表型和回波系统进行测量。在CAMUS数据集上测试DPS-Net时，LV分割获得了良好的性能（艾德和ES的Dice系数分别为0.932和0.928）。通过比较DPS-Net v2与EchoNet-dynamic算法（P= 0.008），观察到研究评价中LV分割的性能更好。DPS-Net与LVEF测量的高相关性和良好一致性相关。对正常心脏和心房颤动、肥厚型心肌病、扩张型心肌病的受试者工作特征曲线下面积分别为0.974、0.948、0.968和0.972。通过使用DPS-Net在具有多种表型的心力衰竭的LV检测和LVEF测量中获得了高性能。在一个大规模的数据集中观察到高性能，这表明DPS-Net在不同的超声心动图系统之间具有高度的自适应性。NEW & NOTEWORTHY一种新的特征提取和融合策略可以提高基于多视图2-D超声心动图序列的自动LVEF评估的准确性。通过使用DPS-Net在具有不同心脏病表型的病例中获得了用于确定心力衰竭的高诊断性能。通过使用DPS-Net获得了高性能的左心室分割，这表明在2DE图像的解释中具有更广泛的应用潜力。
Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from three centers. The impact of three ultrasound machines and three phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson’s method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS data set (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (P= 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968, and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.NEW & NOTEWORTHYA new strategy of feature extraction and fusion could enhance the accuracy of automatic LVEF assessment based on multiview 2-D echocardiographic sequences. High diagnostic performance for the determination of heart failure was obtained by using DPS-Net in cases with different phenotypes of heart diseases. High performance for left ventricle segmentation was obtained by using DPS-Net, suggesting the potential for a wider range of application in the interpretation of 2DE images.