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 < 0.001). Implementing disjunctive rule (nuchal thickness < 10th centile or estimated fetal weight < 10th centile) significantly improved small-for-gestational-age prediction across all growth charts, with balanced accuracy gains of 5.83% in Malaysia (p < 0.05) and 7.75% in Singapore. The best model for predicting small-for-gestational-age was: logistic regression with five variables (abdominal circumference, femur length, nuchal thickness, maternal age, and ultrasound-confirmed gestational age), which achieved an area under the curve of 0.75 for Malaysia cohorts; support vector machine with all variables, achieved area under the curve of 0.81 for Singapore cohorts.

Conclusions: Small-for-gestational-age infants demonstrate significantly reduced second-trimester nuchal thickness. Employing the disjunctive rule enhanced small-for-gestational-age prediction. Logistic regression and support vector machines show superior performance among all models, highlighting the advantages of machine learning. Larger prospective studies are needed to assess clinical utility.