Ultrasound Estimation of Fetal Weight by Artificial Neural Network in Normal Pregnancies
Ultrasound estimation of fetal weight is used for diagnosing intrauterine growth retardation, counseling, differential diagnoses and care management. The aim of the present study was to assess the accuracy of ultrasound birth weight prediction by use of Artificial Neural Network (ANN) model. At First, as the training group, we performed US examinations on 556 healthy singleton fetuses after 12 weeks gestation within 3 days of delivery. Seven input variables were used to construct the ANN model: abdominal circumference (AC), biparietal diameter (BPD), gestational age (GA), Head circumference (HC), femur length (FL), crown coccyx length (CCL) and clavicle length (CL). Then, a total of 209 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 149.77g and 5.32％, respectively. Results show that, artificial neural network (ANN) model can provide better US estimation of fetal weight.
component ultrasound fetal weight estimation artificial neural network
Hanieh Mohammadi Mercedeh Jahanseir Zohreh Allahmoradi Farzaneh Samiee
Dept.Biomedical Engineering Science and Research Branch Islamic Azad University Tehran, Iran DeptBiomedical 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
575-579
2010-11-16（万方平台首次上网日期，不代表论文的发表时间）