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Fetal weight estimation based on deep neural network: a retrospective observational study

BACKGROUND: 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. METHODS: This s...

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Autores principales: Wang, Yifei, Shi, Yi, Zhang, Chenjie, Su, Kaizhen, Hu, Yixiao, Chen, Lei, Wu, Yanting, Huang, Hefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394792/
https://www.ncbi.nlm.nih.gov/pubmed/37533038
http://dx.doi.org/10.1186/s12884-023-05819-8
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author Wang, Yifei
Shi, Yi
Zhang, Chenjie
Su, Kaizhen
Hu, Yixiao
Chen, Lei
Wu, Yanting
Huang, Hefeng
author_facet Wang, Yifei
Shi, Yi
Zhang, Chenjie
Su, Kaizhen
Hu, Yixiao
Chen, Lei
Wu, Yanting
Huang, Hefeng
author_sort Wang, Yifei
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05819-8.
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spelling pubmed-103947922023-08-03 Fetal weight estimation based on deep neural network: a retrospective observational study Wang, Yifei Shi, Yi Zhang, Chenjie Su, Kaizhen Hu, Yixiao Chen, Lei Wu, Yanting Huang, Hefeng BMC Pregnancy Childbirth Research BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05819-8. BioMed Central 2023-08-02 /pmc/articles/PMC10394792/ /pubmed/37533038 http://dx.doi.org/10.1186/s12884-023-05819-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Yifei
Shi, Yi
Zhang, Chenjie
Su, Kaizhen
Hu, Yixiao
Chen, Lei
Wu, Yanting
Huang, Hefeng
Fetal weight estimation based on deep neural network: a retrospective observational study
title Fetal weight estimation based on deep neural network: a retrospective observational study
title_full Fetal weight estimation based on deep neural network: a retrospective observational study
title_fullStr Fetal weight estimation based on deep neural network: a retrospective observational study
title_full_unstemmed Fetal weight estimation based on deep neural network: a retrospective observational study
title_short Fetal weight estimation based on deep neural network: a retrospective observational study
title_sort fetal weight estimation based on deep neural network: a retrospective observational study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394792/
https://www.ncbi.nlm.nih.gov/pubmed/37533038
http://dx.doi.org/10.1186/s12884-023-05819-8
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