Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

Menglin Zhou; Jiansheng JI; Ni Xie; Danqing Chen

Return

Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network.

Author: Menglin ZHOU ¹ ; Jiansheng JI ¹ ; Ni XIE ¹ ; Danqing CHEN ²
Author Information

1. Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
2. Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China. chendq@zju.edu.cn.
Publication Type:Journal Article
MeSH: Adult; Birth Weight; Diabetes, Gestational; Female; Fetal Development; Humans; Infant; Infant, Newborn; Neural Networks, Computer; Pregnancy
From: Journal of Zhejiang University. Science. B 2022;23(5):432-436
CountryChina
Language:English
Abstract: Gestational diabetes mellitus (GDM) is common during pregnancy, with the prevalence reaching as high as 31.0% in some European regions (McIntyre et al., 2019). Dysfunction of the glucose metabolism in pregnancy can influence fetal growth via alteration of the intrauterine environment, resulting in an increased risk of abnormal offspring birth weight (McIntyre et al., 2019). Infants with abnormal birth weight will be faced with increased risks of neonatal complications in the perinatal period and chronic non-communicable diseases in childhood and adulthood (Mitanchez et al., 2015; McIntyre et al., 2019). Therefore, accurate estimation of birth weight for neonates from women with GDM is crucial for more sensible perinatal decision-making and improvement of perinatal outcomes. Timely antenatal intervention, with reference to accurately estimated fetal weight, may also decrease the risks of adverse long-term diseases.