%0 Observational Study
%T Fetal weight estimation based on deep neural network: a retrospective observational study.
%A Wang Y
%A Shi Y
%A Zhang C
%A Su K
%A Hu Y
%A Chen L
%A Wu Y
%A Huang H
%J BMC Pregnancy Childbirth
%V 23
%N 1
%D 2023 Aug 2
%M 37533038
%F 3.105
%R 10.1186/s12884-023-05819-8
%X <B>BACKGROUND: </B>Improving the accuracy of estimated fetal weight (EFW) calculation can contribute to decision-making for obstetricians and decrease perinatal complications. This study aimed to develop a deep neural network (DNN) model for EFW based on obstetric electronic health records.<BR><B>METHODS: </B>This study retrospectively analyzed the electronic health records of pregnant women with live births delivery at the obstetrics department of International Peace Maternity & Child Health Hospital between January 2016 and December 2018. The DNN model was evaluated using Hadlock's formula and multiple linear regression.<BR><B>RESULTS: </B>A total of 34824 live births (23922 primiparas) from 49896 pregnant women were analyzed. The root-mean-square error of DNN model was 189.64 g (95% CI 187.95 g-191.16 g), and the mean absolute percentage error was 5.79% (95%CI: 5.70%-5.81%), significantly lower compared to Hadlock's formula (240.36 g and 6.46%, respectively). By combining with previously unreported factors, such as birth weight of prior pregnancies, a concise and effective DNN model was built based on only 10 parameters. Accuracy rate of a new model increased from 76.08% to 83.87%, with root-mean-square error of only 243.80 g.<BR><B>CONCLUSIONS: </B>Proposed DNN model for EFW calculation is more accurate than previous approaches in this area and be adopted for better decision making related to fetal monitoring.