ArticleJournal of neonatal-perinatal medicine2026
Machine learning demonstrates normal fetal Doppler velocimetry associated with reduced risk of necrotizing enterocolitis among preterm infants with growth restriction.
Article in Journal of neonatal-perinatal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.Pediatric research · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
BackgroundNecrotizing enterocolitis (NEC) is an intestinal ischemic disease that affects preterm infants with fetal growth restriction (FGR). The role of fetal Dopplers in stratifying risk for developing NEC is unclear but the innovative use of machine learning technology may aid in identifying their contribution.MethodsThis is a single center retrospective cohort of 164 infants born before 33 weeks' gestation with FGR from 2016 to 2019. We used machine learning to classify NEC and to evaluate the predictive values of gestational age (GA), birth weight (BW), and the presence of abnormal umbilical artery (UA) Dopplers before delivery.ResultsEarly GA and lower BW strongly predicted NEC. The presence of normal fetal UA Dopplers was heavily weighted in classifying infants unlikely to develop NEC. Fetal UA Dopplers had a 95% specificity (15% sensitivity) for NEC.ConclusionsIf validated, normal fetal UA Doppler studies may identify infants with FGR at low risk for NEC who may avoid conservative NEC-prevention strategies.
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