ArticleBMC pregnancy and childbirth2021
Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study.
Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Bayesian multilevel joint model for predicting neonatal head circumference and body mass index: a case study in Dalahu County, Iran.BMC pregnancy and childbirth · 2026Article
- Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea.Life (Basel, Switzerland) · 2022Article
- Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022Article
- Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling.PloS one · 2022Article
- Predictors of Newborn's Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data.Diagnostics (Basel, Switzerland) · 2021Article
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9 authors.
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Abstract
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.
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