Machine Learning Applications in Male Factor Infertility: Have Cytokines a Role in the Diagnostic Work-Up of Varicocele?

The introduction of Artificial Intelligence (AI) in reproductive medicine has found interesting applications, especially in assisted reproduction techniques, while other potential clinical applications in male fertility are underinvestigated. Varicocele is among the most common andrological diseases and has a known negative impact on spermatogenesis and male fertility through hypoxia and increased oxidative stress. The role of inflammation in varicocele-induced spermatogenesis impairment has also been proposed, although data is scant. Thus, this study aims to re-analyze clinical data from a previous study using advanced ML algorithms to identify patterns previously overlooked by traditional statistics and produce new physio-pathological hypothesis. To achieve the objective, a classification system was developed using multiple implementations of AdaBoost and XGBoost classifiers. Additionally, the LIME explainability module was integrated to provide insights into which features had the most significant impact on the classification process. The accuracy of the model reached peaks of 97%. The results support the hypothesis that IL-8 could be considered a key marker of damage to spermatogenesis in OAT and varicocele subjects. In conclusion, the integration of AI analyses in male fertility research could foster new diagnostic approaches, potentially improving fertility outcomes for affected individuals.