Machine Learning Evaluation of Semen Analysis Could Reveal New Infertility-Related Markers: A Pilot Study

Purpose

To perform a pilot study aiming at evaluating whether machine learning could be a useful model to evaluate semen analysis, improving the diagnostic work-up of male partner of infertile couples. Materials and Methods: A retrospective observational study was conducted using real-world data on male evaluated in routine andrological clinical practice at two Italian tertiary centers. The study utilized two distinct datasets: the first (UNIROMA) encompassed three distinct variables, including semen analysis, sex hormones, and testicular ultrasound parameters. The second dataset (UNIMORE) was constructed incorporating semen analysis, sex hormones, biochemical examinations, and parameters related to environmental pollution. The XGBoost analysis, as part of machine learning techniques, was applied separately to each dataset, as the two datasets did not share a significant overlap in terms of variables.