aceConventional statistical and machine learning-based prediction of neonatal outcomes in late-onset fetal growth restriction
Objectives To evaluate associations between antenatal fetal biometric and Doppler parameters and neonatal outcomes in late-onset fetal growth restriction (FGR), and to evaluate machine learning models for neonatal outcome prediction. Methods This retrospective cohort included 137 singleton pregnancies. Continuous missing values were completed using a single iterative chained-equations procedure. Maternal characteristics, fetal biometric measurements, and Doppler parameters, including umbilical artery pulsatility…
Machine learning models built on fetal biometry, Doppler and maternal data predicted birth weight and neonatal sepsis with moderate accuracy, supporting conventional statistical findings in late-onset FGR.
The authors state the findings should be interpreted in light of the retrospective design and the lack of external validation of the models.
Evidence
- Peer-reviewedJournal of Perinatal Medicine2026-10-07
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Truvace Impact Record TRV-2026-1789, v1: “Conventional statistical and machine learning-based prediction of neonatal outcomes in late-onset fetal growth restriction.” Truvace, 2026-10-18. /record/TRV-2026-1789 (accessed at citation time). sha256 d5b44350f2b7043b…
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