Using Akaike information criterion and minimum mean square error mode in compensating for ultrasonographic errors for estimation of fetal weight by new operators

Abstract

Objectives: 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. Materials and 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). Conclusion: 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.

Keywords

• Akaike information criterion
• artificial neural network
• estimated fetal weight
• minimum mean squared error
• ultrasonography