Department of Electronic Engineering, Fudan University, Shanghai 200433, China.
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, China.
J Healthc Eng. 2017;2017:4797315. doi: 10.1155/2017/4797315. Epub 2017 Oct 23.
Determination of fetal left ventricular (LV) volume in two-dimensional echocardiography (2DE) is significantly important for quantitative analysis of fetal cardiac function. A backpropagation (BP) neural network method is proposed to predict LV volume more accurately and effectively. The 2DE LV border and volume are considered as the input and output of BP neural network correspondingly. To unify and simplify the input of the BP neural network, 16 distances calculated from the border to its center with equal angle are used instead of the border. Fifty cases (forty frames for each) were used for this study. Half of them selected randomly are used for training, and the others are used for testing. To illustrate the performance of BP neural network, area-length method, Simpson's method, and multivariate nonlinear regression equation method were compared by comparisons with the volume references in concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Bland-Altman plots. The ICC and CCC for BP neural network with the volume references were the highest. For Bland-Altman plots, the BP neural network also shows the highest agreement and reliability with volume references. With the accurate LV volume, LV function parameters (stroke volume (SV) and ejection fraction (EF)) are calculated accurately.
二维超声心动图（2DE）中胎儿左心室（LV）容积的测定对胎儿心功能的定量分析具有重要意义。提出了一种反向传播（BP）神经网络方法，以更准确有效地预测 LV 容积。将 2DE LV 边界和容积分别作为 BP 神经网络的输入和输出。为了统一和简化 BP 神经网络的输入，使用从边界到其中心的 16 个等角距离代替边界。这项研究使用了 50 例（每例 40 帧）。随机选择一半用于训练，另一半用于测试。为了说明 BP 神经网络的性能，通过与一致性相关系数（CCC）和组内相关系数（ICC）以及 Bland-Altman 图中的体积参考值进行比较，比较了面积长度法、辛普森法和多元非线性回归方程法。与体积参考值的 BP 神经网络的 ICC 和 CCC 最高。对于 Bland-Altman 图，BP 神经网络与体积参考值的一致性和可靠性也最高。有了准确的 LV 容积，就可以准确计算 LV 功能参数（每搏量（SV）和射血分数（EF））。
