A Comprehensive Review of Polycystic Ovary Syndrome (PCOS): Causes, Diagnosis, Lifestyle, Emerging Therapies, and Explainable AI-Based Detection

Author

Mrs. Gorisetty Nirosha , Gembali Sruthi


Abstract

 Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine and metabolic condition associated with ovulatory dysfunction, clinical or biochemical hyperandrogenism, and polycystic ovarian morphology. Its variable presentation and overlap with other disorders can delay recognition, while reproductive, metabolic, and psychological concerns require coordinated long-term care. This review synthesizes the clinical background and artificial-intelligence approaches described in the accompanying term-paper report and presentation. It examines conventional assessment, lifestyle and medical management themes, machine-learning methods for structured clinical data, deep-learning analysis of ovarian ultrasound images, and explainable AI. Published examples include a web-based machine-learning study using 541 records that reported 94% accuracy after mutual-information feature selection, and a 2026 multimodal study combining ultrasound and clinical attributes through cross-attention. Building on these themes, an explainable hybrid framework is outlined in which clinical and imaging streams are processed separately, fused for decision support, and interpreted using SHAP/LIME and Grad-CAM. The source material also contains a Random Forest training script using nine clinical variables and an 80:20 stratified split; however, it does not provide its own test metrics or demonstrate a validated multimodal model. Accordingly, no new experimental performance is claimed. The review identifies dataset quality, external validation, interpretability, privacy, and clinician oversight as essential requirements before clinical deployment.



Keywords

Polycystic ovary syndrome, PCOS, machine learning, ultrasound imaging, multimodal learning, explainable artificial intelligence, clinical decision support.



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References


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