Predicting adverse perinatal outcomes in small-for-gestational-age fetuses using MRI, ultrasound, and clinical data.

ABSTRACT:

Background

Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Existing sonographic approaches offer limited predictive accuracy. Combining fetal MRI, ultrasound and clinical data may improve perinatal prognostication.

Purpose

To evaluate whether integrating prenatal MRI, ultrasound, and clinical features using machine learning (ML) improves prediction of adverse perinatal outcomes in FGR or small-for-gestational-age (SGA) pregnancies.

Materials and methods

This single-center study included prospectively enrolled FGR/SGA and retrospectively included appropriate-for-gestational-age cases, with follow-up through neonatal discharge. Twenty-seven features from MRI, ultrasound, and clinical data were used in the final analysis. Seven ML classifier

SUBMITTER: Rabinowich A 

PROVIDER: S-EPMC12483154 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature