Efficient fetal size classification combined with artificial neural network for estimation of fetal weight - Yueh-Chin Cheng; Gwo-Lang Yan; Yu Hsien Chiu; Fong-Ming Chang; Chiung-Hsin Chang; Kao-Chi Chung (Elsevier, 2012)
Objectives: A novel analysis was undertaken to select a significant ultrasonographic parameter (USP) for classifying fetuses to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation. Methods: In total, 2127 singletons were examined by prenatal ultrasound within 3 days before delivery. First, correlation analysis was used to determine a significant USP for fetal grouping. Second, K-means algorithm was utilized for fetal size classification based on the selected USP. Finally, stepwise regression analysis was used to examine input parameters of the ANN model. Results: The estimated fetal weight (EFW) of the new model showed mean absolute percent error (MAPE) of 5.26 ± 4.14% and mean absolute error (MAE) of 157.91 ± 119.90 g. Comparison of EFW accuracy showed that the new model significantly outperformed the commonly-used EFW formulas (all p < 0.05). Conclusion: We proved the importance of choosing a specific grouping parameter for ANN to improve EFW accuracy.