Key result
Backpropagation neural networks outperform traditional echocardiography for predicting fetal LV volume.
Why the study?
Does a backpropagation neural network method improve the accuracy of fetal left ventricular volume determination from 2D echocardiography compared to traditional methods?
Does a backpropagation neural network method improve the accuracy of fetal left ventricular volume determination from 2D echocardiography compared to traditional methods?
A backpropagation neural network method provides more accurate and reliable determination of fetal left ventricular volume from 2D echocardiography compared to traditional geometric methods.
Answers are grounded in this paper and cite their sources.
May improve fetal LV volume estimation accuracy; hypothesis-generating and requires prospective validation before clinical adoption.
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.
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Yu et al. (2017) studied Fetal left ventricular volume determination (n=50). Backpropagation (BP) neural network method vs. Area-length method, Simpson's method, and multivariate nonlinear regression equation method was evaluated on Concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Bland-Altman plots for LV volume prediction. A backpropagation neural network method demonstrated the highest agreement and reliability for predicting fetal left ventricular volume compared to traditional echocardiographic methods.
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