基于人工神经网络的足月胎儿体重预测方法

Estimation of Fetal Weight on the Basis of Neural Network

对胎儿体重的预测在产科临床上具有非常重要的意义,传统上采用回归分析方法预测胎儿体重,存在可靠性差等缺点。本研究采用反向传播(BP)人工神经网络方法预测胎儿体重,实验中采用双顶径、小脑横径、腹围、肝脏长度、股骨长度、股骨皮下脂肪厚度、孕龄等参数作为BP神经网络的输入参数,网络由输入层、隐含层和输出层三部分组成。对109例临床资料进行预测,结果为:训练组预测符合率达89.77%,平均绝对误差104.22g,平均相对误差3.24%;验证组预测符合率达76.19%,平均绝对误差190.84g,平均相对误差5.60%。表明人工神经网络预测胎儿体重方法十分有效,准确性高于回归方程。

The ultrasonic estimation of fetal weight at delivery is of important prognostic significance in obstetrical practice. The convertional regression formulas used for estimating fetal weight have the disadvantage of less reliability. In this study, we used the back propagation neural network （BP） to estimate Fetal Weight. Some input variables were adopted in constructing the BP model： biparietal diameter （BPD）, cerebellum transverse diameter （TCD）, abdominal circumference （AC）, liver length（LL）, femur length（FL）, fetal thigh soft tissue thickness （FSTT）, and gestational age（GA）. The fetal weights of 109 singleton fetuses were estimated. In the training group and validation group, coincidence rates were 89.77% and 76.19% respectively. The results show that the estimation based on neural network is more accurate than that by regression method. GA,its unit is not week but day in our formulas, is very valuable in combination with other ultrasonic parameters on estimation.