Deep Learning-Based Prediction of IVF Success: A Transformer Model Approach

Deep Learning-Based Prediction of IVF Success

Abstract

Introduction: Predicting the success of assisted reproductive technology (ART) remains a significant challenge due to the complex interplay of clinical, embryological, and demographic factors. This study aimed to develop and evaluate machine learning models, particularly deep learning-based approaches, to identify key predictors of ART success and improve outcome prediction accuracy. 

 Methods: A retrospective study was conducted on 500 infertile couples undergoing ART treatment between 2019 and 2024. A comprehensive dataset, including 84 clinical, embryological, and demographic variables, was analyzed. The key predictors included endometrial thickness, endometrial pattern, embryo transfer day, and hormonal markers (PRL, LH). Four machine learning models were implemented: Decision Tree, Random Forest, XGBoost, and a Transformer-Based Model. Data preprocessing involved feature selection, missing data handling, normalization, and oversampling techniques to address class imbalance. The models were trained and validated using k-fold cross-validation, and performance was assessed using accuracy, precision, recall, and F1 score. 

 Results: The Transformer-Based Model achieved the highest accuracy (99.7%), outperforming traditional machine learning models. Endometrial pattern (r = 0.69) and endometrial thickness (r = 0.82) were the strongest predictors of ART success, emphasizing the dominant role of uterine factors. While female age and infertility duration had a weak negative correlation, male infertility factors and lifestyle variables (smoking, alcohol consumption) showed minimal predictive significance. Model-based feature importance confirmed uterine and embryological factors as the primary determinants of ART success, suggesting a shift in treatment focus. 

 Conclusions: This study highlights the superiority of deep learning models in ART success prediction, with uterine factors emerging as the strongest predictors. Integrating AI-driven predictive models into clinical practice can enable personalized ART treatment, improved patient counseling, and optimized embryo transfer strategies, ultimately enhancing fertility outcomes.