Mattar CNZ

Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor

Objective: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data.