Advancement in early diagnosis of polycystic ovary syndrome: biomarker-driven innovative diagnostic sensor.
Journal:
Mikrochimica acta
PMID:
40310524
Abstract
Polycystic ovary syndrome (PCOS) is a heterogeneous multifactorial endocrine disorder that affects one in five women around the globe. The pathology suggests a strong polygenic and epigenetic correlation, along with hormonal and metabolic dysfunction, but the exact etiology is still a mystery. The current diagnosis is mostly based on Rotterdam criteria, which resulted in a delayed diagnosis in most of the cases, leading to unbearable lifestyle complications and infertility. PCOS is not new; thus, constant efforts are made in the field of biomarker discovery and advanced diagnostic techniques. A plethora of research has enabled the identification of promising PCOS diagnostic biomarkers across hormonal, metabolic, genetic, and epigenetic domains. Not only biomarker identification, but the utilization of biosensing platforms also renders effective point-of-care diagnostic devices. Artificial intelligence also shows its power in modifying existing image-based analysis, even developing symptom-based prediction systems for the early diagnosis of this multifaceted disorder. This approach could affect the future management and treatment direction of PCOS, decreasing its severity and improving the reproductive life of women. The rationale of the current review is to identify the advancements in understanding the pathophysiology through biomarker discovery and the implementation of modern analytical techniques for the early diagnosis of PCOS.