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has been cited by the following article:

• TITLE: Ultrasound estimation of fetal weight in twins by artificial neural network

  AUTHORS: Hanieh Mohammadi, Meshkat Nemati, Zohreh Allahmoradi, Hoda Forghani Raissi, Somayeh Saraf Esmaili, Ali Sheikhani

  KEYWORDS: Ultrasound; Fetal Weight Estimation; Twin; Artificial Neural Network

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.1, January 29, 2011

  ABSTRACT: This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy singleton fetuses within 3 days of delivery. Three input variables were used to construct the ANN model: abdominal circumference (AC), ab-dominal diameter (AD), biparietal diameter (BPD). Then, a total of 121 twin fetuses were assessed sub-sequently as the validation group. In validation group, the mean absolute error and the mean absolute per-cent error between estimated fetal weight and actual fetal weight was 261.77 g and 7.81%, respectively. Results show that, twin estimation of birth weight by ultrasound correlates fairly well with the actual weights of twin fetuses.