Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models

OBJECTIVE: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.

METHOD: This retrospective study involved singleton pregnancies at University Malaya Medical Centre, Malaysia, developed a nuchal thickness chart and evaluated its predictive value for small-for-gestational-age using Malaysian and Singapore cohorts. Predictive performance using conjunctive (AND)/disjunctive (OR) rule-based algorithms was assessed. Seven machine learning models were trained on Malaysia data and evaluated on both Malaysia and Singapore cohorts.

RESULTS: 5519 samples were collected from the University Malaya Medical Centre. Small-for-gestational-age infants exhibit significantly lower nuchal thickness (small-for-gestational-age: 4.57 [1.04] mm, appropriate-for-gestational-age: 4.86 [1.06] mm, p