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Journal of Healthcare Engineering
Volume 2017, Article ID 4797315, 9 pages
Research Article

Determination of Fetal Left Ventricular Volume Based on Two-Dimensional Echocardiography

1Department of Electronic Engineering, Fudan University, Shanghai 200433, China
2Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, China
3Department of Ultrasound, The First Maternal and Infant Health Care Hospital, Shanghai 200433, China

Correspondence should be addressed to Yi Guo; nc.ude.naduf@iyoug and Yuanyuan Wang; nc.ude.naduf@gnawyy

Received 4 June 2017; Accepted 13 September 2017; Published 23 October 2017

Academic Editor: Emiliano Schena

Copyright © 2017 Li Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.