Infertility is a multifactorial reproductive disorder affecting millions of couples worldwide and remains a major challenge in reproductive medicine. In recent years, exosomes – small extracellular vesicles enriched with bioactive proteins, lipids, and nucleic acids – have emerged as key regulators of reproductive physiology and promising therapeutic agents for infertility-related disorders. Exosomes derived from the mesenchymal stem cells and reproductive tissues have demonstrated the ability to restore ovarian function, improve spermatogenesis, modulate inflammation, and enhance tissue regeneration in preclinical models of polycystic ovary syndrome, premature ovarian insufficiency, and male factor infertility. However, the complexity and heterogeneity of exosomal cargo present significant challenges for biomarker discovery, therapeutic optimization, and clinical translation. Machine learning (ML) has rapidly gained attention as a powerful analytical tool capable of handling high-dimensional exosomal datasets and integrating molecular, imaging, and clinical information. Recent studies have applied ML algorithms to identify infertility-associated exosomal biomarkers, improve noninvasive diagnostic accuracy, and objectively evaluate therapeutic responses to exosome-based interventions. ML-assisted approaches, including pattern recognition of exosomal miRNA profiles, Raman spectroscopy–based diagnostics, and histopathological image analysis, have significantly enhanced the precision and interpretability of infertility research. Furthermore, emerging evidence suggests that ML may enable personalized exosome therapies by predicting patient-specific responses and optimizing exosome source, dosage, and delivery strategies. This minireview summarizes recent advances (2021–2025) in the application of ML to exosome-based diagnostics and therapeutics for infertility, with emphasis on preclinical and early clinical studies. We also discuss current limitations, including data standardization, model interpretability, and translational barriers, and highlight future directions toward artificial intelligence-driven precision reproductive medicine.
Fazaeli et al. (Wed,) studied this question.