title: Using Akaike information criterion and minimum mean square error mode in compensating for ultrasonographic errors for estimation of fetal weight by new operators.
abstract: OBJECTIVE
  The accuracy of ultrasound (US) measurements is operator dependent. In order to decrease the operator-dependent errors in estimated fetal weight (EFW), a model selection analysis was undertaken to select significant compensation weighting factors on ultrasonographic parameters 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 US within 3 days before delivery for ANN development, and another 100 cases were selected from new operators for evaluation. First, correlation analysis was used to analyze the differences between the prenatal and postnatal parameters. Second, Akaike information criterion (AIC) was used to determine the number of database partition and optimal weightings for compensating the input parameters of the ANN model. Finally, minimum mean squared error (MMSE) mode was utilized to determine the optimal EFW.
  
  
  RESULTS
  EFW of the proposed compensation model using AIC and MMSE showed mean absolute percent error of 5.1 ± 3.1% and mean absolute error of 158.9 ± 96.2 g. When comparing the accuracy of EFW, our model using AIC and MMSE was superior to those conventional EFW formulas (all p < 0.05).
  
  
  CONCLUSIONS
  We proved that performing the parameter compensation (by AIC) and model compensations (by MMSE) for the ANN model can improve EFW accuracy. Our AIC-MMSE model of EFW will contribute to the improvement of accuracy when adding new US datasets measured by new operators.
authors.affiliation: Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
authors.forename: Yueh-Chin
authors.initials: YC
authors.lastname: Cheng
authors.forename: Yu Hsien
authors.initials: YH
authors.lastname: Chiu
authors.forename: Hsien-Chang
authors.initials: HC
authors.lastname: Wang
authors.forename: Fong-Ming
authors.initials: FM
authors.lastname: Chang
authors.forename: Kao-Chi
authors.initials: KC
authors.lastname: Chung
authors.forename: Chiung-Hsin
authors.initials: CH
authors.lastname: Chang
authors.forename: Kuo-Sheng
authors.initials: KS
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D005260:Female; D020567:Fetal Weight; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015588:Observer Variation; D011247:Pregnancy; D012044:Regression Analysis; D012189:Retrospective Studies; D016216:Ultrasonography, Prenatal / Q000379:methods*