Observational studyJournal of perinatology : official journal of the California Perinatal Association2026
AI to predict extrauterine growth restriction during transitional nutrition of preterm infants: a retrospective study.
Observational study in Journal of perinatology : official journal of the California Perinatal Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine Learning-Driven Computer Vision System for Automated Fat and Energy Quantification in Human Milk Microcapillaries.Sensors (Basel, Switzerland) · 2026Article
- Observational
- Predicting serum phosphate levels in very preterm infants using machine learning.Frontiers in pediatrics · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
objectiveExtrauterine growth restriction (EUGR) affects 30-97% of preterm infants and is associated with poor outcomes. We used machine learning (ML) to assess how clinical and nutritional factors, particularly during the transition from parenteral to enteral nutrition, influence EUGR. STUDY
designThis retrospective observational study included 1165 patients (46% with EUGR) born below 33 weeks' gestation or 1500 g. We developed 10 models to predict EUGR combining two sets of features (all and nutritional features only) across five subgroups of patients (all, extremely preterm, very preterm, moderately preterm, small for gestational age).
resultsModel accuracy was 0.71 (F1-score = Recall = AUROC = 0.71, Precision = 0.72) with nutritional features and 0.79 (F1-score = AUROC = 0.79, Precision = 0.80, Recall = 0.79) with all features. Lower EUGR risk was linked to female sex, higher growth velocity, and lipid intake in week one. Influential factors differed by subgroup.
conclusionML models accurately predicted EUGR across preterm subgroups, highlighting the role of early nutritional and clinical variables.
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