Integrating Artificial Intelligence into Reproductive Medicine: A Predictive Algorithm for Noninvasive Live Birth Assessment.

OBJECTIVE: To develop and evaluate a machine learning model integrating clinical, embryological, morphokinetic, and semen-related variables to estimate the probability of live birth after in vitro fertilization (IVF). METHODS: This retrospective study included data from 375 IVF cycles performed between 2013 and 2022. Morphokinetic, clinical, and semen-related parameters were analyzed using four machine learning algorithms: logistic regression, support vector machine, neural network, and extreme gradient boosting (XGBoost). Model performance was assessed using the area under the receiver operating characteristic curve (AUC). The best-performing model underwent Bayesian hyperparameter optimization and synthetic data augmentation. Feature importance was evaluated to identify the variables contributing most strongly to live birth prediction. RESULTS: Among the initial models, XGBoost demonstrated the best and most stable predictive performance, with an AUC above 0.60. Following hyperparameter optimization and data augmentation, the final XGBoost model achieved an AUC of 0.927. The most relevant predictive variables were transferred embryo class, timing of second polar body extrusion, anti-Müllerian hormone concentration, female age, and body mass index. These findings supported the development of an application designed to estimate individualized probabilities of live birth after IVF treatment. CONCLUSION: An artificial intelligence-based model integrating clinical, embryological, morphokinetic, and semen-related variables may provide a useful noninvasive approach to individualized live birth prediction after IVF. Prospective external validation is required to confirm its clinical applicability, assess calibration, and reduce the risk of overestimating treatment success.