title: Efficient fetal size classification combined with artificial neural network for estimation of fetal weight.
abstract: OBJECTIVE
  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).
  
  
  CONCLUSIONS
  We proved the importance of choosing a specific grouping parameter for ANN to improve EFW accuracy.
authors.affiliation: Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
authors.forename: Yueh-Chin
authors.initials: YC
authors.lastname: Cheng
authors.forename: Gwo-Lang
authors.initials: GL
authors.lastname: Yan
authors.forename: Yu Hsien
authors.initials: YH
authors.lastname: Chiu
authors.forename: Fong-Ming
authors.initials: FM
authors.lastname: Chang
authors.forename: Chiung-Hsin
authors.initials: CH
authors.lastname: Chang
authors.forename: Kao-Chi
authors.initials: KC
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D000886:Anthropometry / Q000379:methods*; D001724:Birth Weight; D005260:Female; D020567:Fetal Weight; D005865:Gestational Age; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011247:Pregnancy; D012044:Regression Analysis; D012189:Retrospective Studies; D018709:Statistics, Nonparametric; D016216:Ultrasonography, Prenatal