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

ML could potentially improve the prediction of SGA at birth from second-trimester measurements, and demonstrated reduced NF to be an important predictor. Early prediction of SGA allows closer clinical monitoring, which provides an opportunity to discover any underlying diseases associated with SGA.