国际妇产科学杂志

国际妇产科学杂志 ›› 2017, Vol. 44 ›› Issue (5): 560-564.

• 论著 • 上一篇    下一篇

超声预测足月胎儿出生体质量方法的探究

张曼，马琳，孙玉伟   

  1. 063000  河北省唐山市，华北理工大学附属医院超声科
  • 收稿日期:2017-03-01 修回日期:2017-06-19 出版日期:2017-10-15 发布日期:2017-10-25
  • 作者简介:2017-07-28

Research on the Methods of Estimation of Fetal Weight Based on Ultrasound

ZHANG Man，MA Lin，SUN Yu-wei   

  1. Department of Ultrasound，North China University of Science and Technology Affiliated Hospital，Tangshan 063000，Hebei Province，China
  • Received:2017-03-01 Revised:2017-06-19 Published:2017-10-15 Online:2017-10-25

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摘要： 目的：比较传统预测方法及BP人工神经网络（BP-ANN）预测胎儿体质量的准确性，探讨预测足月胎儿出生体质量的最佳模型。方法：选取2015年11月—2016年7月产前0~5 d在我院进行超声检查并分娩的单胎足月孕妇224例，收集临床及超声资料。根据胎儿实际体质量，分为非巨大儿组（胎儿体质量＜4 000 g，n=183）和巨大儿组（胎儿体质量≥4 000 g，n=41）。使用误差（绝对误差、相对误差）评价不同方法预测胎儿体质量的准确性。结果：超声参数、联合参数（临床+超声）的BP-ANN在不同组别预测胎儿体质量的误差小于传统预测方法（P＜0.05），联合参数的BP-ANN在巨大儿组中随着训练样本数的增加误差降低（P＜0.05）；联合参数的BP-ANN在非巨大儿组、整体224例中误差均小于临床参数、超声参数（P＜0.05），巨大儿组中联合参数、超声参数的BP-ANN误差小于临床参数（P＜0.05），但联合参数与超声参数比较差异无统计学意义（P＞0.05）。结论：BP-ANN预测胎儿体质量准确性优于传统预测方法；预测足月胎儿出生体质量的最佳模型为联合参数的BP-ANN。

关键词: 足月分娩, 胎儿体重, 模型, 理论, 神经网络（计算机）, BP人工神经网络

Abstract: Objective：To compare the accuracy of traditional prediction method and back propagation artificial neural network (BP-ANN) for estimation of fetal weight (EFW), to explore the best model for EFW. Methods：A total of 224 term pregnant women with singleton between November 2015 and July 2016 in North China University of Science and Technology Affiliated Hospital were included in the present study. They were divided into non-macrosomia group (FW＜4 000 g, n=183) and macrosomia group (FW≥4 000 g, n=41). An ultrasound examination was performed 0~5 days before delivery. Clinical data and ultrasound data were collected. Results：Reduced errors were seen when measurements were obtained by ultrasonic parameter and combined parameter BP-ANN than traditional prediction method (P＜0.05). In macrosomia group, the error of combined parameter BP-ANN decreased with the increase of the number of training samples (P＜0.05). For BP-ANN, less error were seen when measurements were obtained by combined parameter than ultrasonic parameter and clinical parameters in non-macrosomia group and overall (P＜0.05). Error of combined parameter BP-ANN and ultrasonic parametric network were less than those of clinical parameter BP-ANN (P＜0.05), but there was no significant difference between them (P＞0.05). Conclusions：BP-ANN is better than traditional prediction method for EFW. The best model for EFW is combined parameter BP-ANN.

Key words:  Term birth, Fetal weight, Models, theoretical, Neural networks （computer）, Back propagation artificial neural network