Computational Modelling in Polycystic Ovary Syndrome: A Comprehensive Review
Abstract
Background: Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic condition that affects up to 15% of reproductive-age women. Traditional diagnostic and treatment frameworks struggle to address the syndrome's heterogeneity. The development of artificial intelligence (AI), in-silico modelling, and virtual patient simulations has created a new paradigm for studying PCOS pathogenesis and optimising individualised care. Purpose: This review synthesizes current advances in AI-driven virtual patient modelling, machine learning prediction, systems-biology simulations, and computational pharmacology applied to PCOS. Methods: A Narrative style review was carried out, reviewing peer-reviewed studies on AI, predictive modelling, virtual trials, machine learning, and in-silico pharmacokinetics. 50 to 60 consecutive papers covering the entire computational spectrum in PCOS were thoroughly examined. Results: Virtual patient frameworks accurately replicated hormonal oscillations, metabolic states, and therapy outcomes. Regression-based models identified significant determinants of symptom severity. In-silico ADMET analysis determined the safety and drug-likeness of ovulation induction treatments. Systems biology models revealed that nutraceutical combinations have synergistic benefits. Machine learning models applied to EHR data demonstrated great diagnosis accuracy (AUCs up to 0.85). Conclusion: AI-driven computational techniques provide excellent tools for analysing PCOS…
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