A Study On Estimating Fetal Weight And The Mode Of Delivery Using Artificial Neural Network

Objective:To investigate the effect of Artificial Neural Networks in predicting fetal weight and labor mode.Methods:1.Fetal weight predictionThe 226 cases of full-term singleton pregnancy without complication are divided into two groups,100 samples(female and male fetuses each 50) for training and 126 samples(female and male fetuses each 63) for testing.Three groups of neural networks are composed:(1) Joint Parameter Method using the height,weight, abdominal circumference and uterine fundal height of pregnant women as well as the biparietal diameter(BPD),femur length(FL) and amniotic fluid pool depth(AFD) of fetuses under B-ultrasonography;(2) Maternal Parameter Method using the height, weight,abdominal circumference and uterine fundal height of pregnant women;(3) Fetal Parameter Method using the BPD,FL and AFD of fetuses;The neural networks are then trained and tested upon the above samples.2.Labor mode predictionAll investigating 220 cases are collected out of primiparas in full-term singleton pregnancy without complication from Jinan Central Hospital.Among them,58 are spontaneous labor,56 lateral incision of perineum,48 pull method of fetal head,and 58 caesarean sections.The caesarean section cases are regardless of cephalic presentation dystocia and should be those who had tried enough.All samples are divided randomly into two groups:104 for training and 116 for testing.Eleven parameters are chosen:8 maternal ones of the height,weight,abdominal circumference and uterine fundal height of pregnant women as well as the biparietal diameter(BPD),femur length(FL) and amniotic fluid pool depth(AFD) of fetuses under B-ultrasonography;3 ultrasonic ones of the BPD,FL and AFD of fetuses.One Output Value method and Four Output Classification method are designed to create the neural networks. Results:1.Fetal weight prediction(1) Among the three groups,the Joint Parameter Method has the highest predicting accuracy of 84.94%,the Maternal Parameter Method 83.45%and the fetal parameter method 80.80%.Variance P is less than 0.01.(2) Among Joint Parameter Method,uterine fundal heiglit has the highest impact factor of 28.6%.The second comes the height of pregnant women with 27.6%.The third femur length(FL) 23.3%and amniotic fluid pool depth(AFD) 8.1%.(3) Boy Parameter method produces a better prediction precision of 89.07 than Girl Parameter method of 80.84%,and at the same time better than the mixing method.Variance analysis illustrates significance with P＜0.01.2.Labor mode predictionOne Output Value method reaches a total error rate of 38.33%and Four Output Classification method with a favorable 33.34%.Four Output Classification method gives an accuracy of 81.18%for vaginal birth and 19.35%for caesarean section.Conclusions:The fetal weight estimation and labor mode prediction using artificial neural networks show potential research value and application prospect.A subtle combination of maternal and fetal parameters is critical to build up an effective network.Sex-specific research can produce better prediction.Amnion may reduce the prediction precision.