Ultrasound estimation of fetal weight with the use of computerized artificial neural network model.
The aim of this study was to test if the computerized artificial neural network (ANN) model could improve ultrasound (US) estimation of fetal weight over estimation with the other commonly used formulas generated from regression analysis. First, as the training group, we performed US examinations on 991 singleton fetuses within 3 days of delivery. Six input variables were used to construct the ANN model: biparietal diameter (BPD), occipitofrontal diameter (OFD), abdominal circumference (AC), femur length (FL), gestational age and fetal presentation. Second, a total of 362 fetuses were assessed subsequently as the validation group. In this training group, the ANN model was better than the other compared formulas in fetal weight estimation (n = 991, mean absolute error 183.83 g, mean absolute percent error 6.02%, all p < 0.0001). In addition, the validation group further proved the results (n = 362, mean absolute error 179.91 g, mean absolute percent error 6.15%, all p < 0.005). In conclusion, the computerized artificial neural network (ANN) model could provide better US estimation of fetal weight than estimations by means of commonly used formulas generated from regression analysis.
Chuang
Louise
L
Department of Obstetrics and Gynecology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan.
Hwang
Jeng-Yang
JY
Chang
Chiung-Hsin
CH
Yu
Chen-Hsiang
CH
Chang
Fong-Ming
FM
D005260:Female; D020567:Fetal Weight; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011247:Pregnancy; D012044:Regression Analysis; D016216:Ultrasonography, Prenatal