Efficient fetal size classification combined with artificial neural network for estimation of fetal weight

Taiwan J Obstet Gynecol. 2012 Dec;51(4):545-53. doi: 10.1016/j.tjog.2012.09.009.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Anthropometry / methods*
  • Birth Weight
  • Female
  • Fetal Weight*
  • Gestational Age
  • Humans
  • Male
  • Neural Networks, Computer*
  • Predictive Value of Tests
  • Pregnancy
  • Regression Analysis
  • Retrospective Studies
  • Statistics, Nonparametric
  • Ultrasonography, Prenatal*