Abstract

Objective: To assess whether machine learning (ML) offers improved birth weight prediction accuracy, since despite numerous models, the Hadlock formula remains the clinical standard.

Methods: A multicenter retrospective study analyzed data from 9674 singleton pregnancies with estimated fetal weight (EFW) within 7 days of delivery. ML models-Linear Regression, Decision Tree, Random Forest, LightGBM, XGBoost, and Neural Networks-were trained using ultrasound and maternal features. Performance was measured by mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), accuracy, precision, recall, and F1-score for percentile categories.

Results: LightGBM and XGBoost outperformed Hadlock in overall weight estimation (MAPE ~0.065; RMSE ~252; MAE ~190). For birth weight percentiles (<3rd, <10th, >90th, >97th), ML showed marginal or comparable improvement. LightGBM had higher accuracy and F1 for extreme percentiles, whereas Hadlock showed slightly better recall in some cases.

Conclusion: ML models, especially LightGBM and XGBoost, enhanced overall weight prediction but offered limited gains in identifying percentile-based risk. The Hadlock formula remains a strong tool for categorizing at-risk fetuses.