Machine learning prediction of IVF outcomes in unexplained infertility

To develop and evaluate machine learning (ML) models to predict clinical pregnancy (CP) outcomes in couples with unexplained infertility (UI) undergoing in vitro fertilization (IVF), thereby enhancing early treatment decision-making and patient counseling.

A retrospective analysis was conducted on 145 couples diagnosed with UI and treated at a single-center IVF clinic. Live birth data were not available; therefore, CP was used as the primary outcome. Demographic, clinical, and laboratory parameters were collected, and five ML algorithms-light gradient boosting machine (LGBM), random forest, logistic regression, extra trees classifier, and eXtreme gradient boosting-were applied to predict CP outcomes. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Permutation-based feature importance analysis was performed to identify key predictive variables.

Among the evaluated models, the LGBM model achieved the highest test-set ROC-AUC (0.712) and an accuracy of 65.2%. Given the small test sample and the lack of external validation, these findings should be interpreted as exploratory. Variables receiving the highest permutation feature-importance scores included total gonadotropin dose, initial gonadotropin dose, total sperm count, progressive motile sperm count, and infertility duration. These findings should be interpreted as exploratory and do not establish that these variables are independent predictors of CP.

This study presents the first ML-based approach tailored to predicting CP outcomes in couples with UI undergoing IVF. The results highlight the potential of artificial intelligence to inform personalized reproductive care. These exploratory findings demonstrate the feasibility of applying ML methods in UI. Larger prospective multicenter studies are required to determine whether clinically useful prediction models can be developed.