Prediction of newborn’s body mass index using nationwide multicenter ultrasound data: a machine-learning study

BMC Pregnancy and Childbirth

Kwang-Sig Lee¹  Mi Hye Park²  Se Jin Lee³  Sunghun Na³  Ho Yeon Kim⁴  Ki Hoon Ahn⁴  Han Sung Hwang⁵  Sung Ok Kwon⁶ 

[1] AI Center, Korea University College of Medicine, Seoul, South Korea;Department of Obstetrics and Gynecology, Ewha Medical Institute, Ewha Medical Center, Ewha Womans University College of Medicine, Seoul, South Korea;Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea;Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, 02841, Seoul, South Korea;Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, South Korea;Department of Preventive Medicine, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea;

关键词: Newborn;    Body mass index;    Estimated fetal weight;    Abdominal circumference;   

DOI  :  10.1186/s12884-021-03660-5

来源: Springer

【 摘 要 】

BackgroundThis study introduced machine learning approaches to predict newborn’s body mass index (BMI) based on ultrasound measures and maternal/delivery information.MethodsData came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn’s BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later.ResultsBased on variable importance from the random forest, major predictors of newborn’s BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn’s BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error.ConclusionsThis is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns’ BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn’s BMI alongside gestational age at delivery and maternal BMI at delivery.

【 授权许可】

CC BY