Ultrasound Estimation of Fetal Weight by Artificial Neural network Using crown coccyx lenght
Estimation of fetal weight (EFVV) is an important component of pregnancy care management, for example in counseling, differential diagnoses and planning the mode of birth. The object of present study was to determine using CCL as a new parameter within computerized artificial neural network (ANN) model could improve ultrasound (US) estimation of fetal weight. At First, as the training group, we performed US examinations on 556 healthy singleton fetuses after 12 weeks within 3 days of delivery. Five input variables were used to construct the ANN model: biparietal diameter (BPD), occipitofrontal diameter (OFD), abdominal circumference (AC), femur length (FL) and crown coccyx length (CCL). Then, a total of 181 fetuses were assessed subsequently as the validation group. In validation group, the mean absolute error and the mean absolute percent error between estimated fetal weight and actual fetal weight was 172.57 g and 6.11％, respectively. Results show that, the CCL as a new parameter in this artificial neural network (ANN) model can provide better US estimation of fetal weight.
component ultrasound fetal weight estimation crown coccyx length artificial neural network
Hanieh Mohammadi Somayeh Saraf Esmaili Mercedeh Jahanseir Zohreh Allahmoradi Farzaneh Samiee
Dept. Biomedical Engineering Science and research Branch, Islamic Azad University Tehran, Iran
国际会议
2010 International Conference on Measurement and Control Engineering(2010年IEEE测量与控制工程国际会议 ICMCE2010)
成都
英文
518-522
2010-11-16（万方平台首次上网日期，不代表论文的发表时间）