Middle East Fertility Society Journal · Published 2026-07-20 · DOI 10.1186/s43043-026-00354-w
Junbiao Mao, Shuhong Luo, Junling Wang, Ben Yuan
Abstract Background Selecting an appropriate gonadotropin starting dose in GnRH antagonist protocols remains challenging due to substantial inter-individual variability in ovarian response and the need to balance treatment efficacy with safety. Methods In this retrospective cohort study, women undergoing IVF/ICSI with GnRH antagonist protocols were included. Machine learning models, including XGBoost and Random Forest, were developed to predict oocyte yield and moderate-to-severe ovarian hyperstimulation syndrome (OHSS) using baseline clinical and hormonal variables. A multi-objective optimization framework incorporating a composite score (oocyte yield − 5.0 × OHSS probability) was applied to derive individualized starting doses, balancing efficacy against safety. Results A total of 770 cycles were included in the final analysis. The model demonstrated predictive performance for oocyte yield (R² = 0.523, RMSE = 4.75) and moderate discrimination for moderate-to-severe OHSS (AUC = 0.597). Feature importance analysis identified AMH, AFC, and age as the most influential predictors. Dose-response simulations revealed substantial inter-individual variability in optimal dosing, with higher ovarian reserve patients requiring lower starting doses to achieve favorable outcomes while minimizing OHSS risk. Conclusions This study presents a machine learning-based multi-objective optimization framework for individualized gonadotropin dosing, offering clinically actionable decision support and advancing precision medicine in assisted reproduction.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →
Mao, J., Luo, S., Wang, J., et al. (2026). A machine learning-based framework for predicting ovarian response and OHSS risk in GnRH antagonist IVF/ICSI cycles: a multi-objective optimization approach. Middle East Fertility Society Journal. https://doi.org/10.1186/s43043-026-00354-w