Unraveling PCOS: Exploring its causes and diagnostic challenges

Abstract

Abstract Women in the reproductive age range are usually affected with Polycystic Ovary Syndrome (PCOS), a complex and multifaceted condition. Anovulation, hyperandrogenism, and metabolic difficulties like hyperglycemia, hypertension, and obesity in women are all manifestations of this condition, which also affects the reproductive system. The National Institutes of Health in the 1990s, Rotterdam in 2003, and Androgen Excess Polycystic Ovary Syndrome in 2009 all contributed to the evolution of the diagnostic criteria for PCOS. The 2003 Rotterdam criteria are currently the most generally used criteria. They call for at least two of the three criteria – irregular menstrual periods, polycystic ovary morphology on imaging, and hyperandrogenism – either clinically or biochemically – to be present in order to diagnose PCOS. It is currently being suggested that the anti-Müllerian hormone in serum be used instead of follicular count as an official indicator of polycystic ovarian morphology/PCOS. Hyperandrogenism and irregular periods are essential components in determining PCOS in adolescent patients. More recently, it has been shown that artificial intelligence, especially machine learning, holds great promise for detecting and predicting PCOS with high accuracy, potentially assisting in early management and treatment decisions. Examining the underlying mechanisms, clinical symptoms, and challenges involved in making a diagnosis of PCOS in females is the premise of this review article.