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

• 作者: Menglin ZHOU Jiansheng JI Ni XIE Danqing CHEN
• 来源期刊: 浙江大学学报（英文版）（B辑：生物医学和生物技术）
• 年，卷（期）: 2022,（5）
• 所属期刊栏目: Correspondence
• 页码范围: 432-436,后插1-后插5
• 页数: 1页
• 语种: 英文

摘要

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 esti-mation 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.