Digital Twins for Polycystic Ovary Syndrome: Toward Personalised and Predictive Care
Ruqaiyyah Siddiqui, Naveed Ahmed KhanABSTRACT
Background
Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with reproductive, metabolic and psychological complications. Despite its prevalence, clinical management remains largely reactive and symptom driven, reflecting limited capacity to predict individual disease trajectories or treatment responses. Existing risk stratification and machine learning approaches have improved phenotyping and outcome prediction but are generally static, population based and unable to capture the complex longitudinal interactions that characterise PCOS.
Main Body
Digital twin technology, defined as a continuously updating, patient specific computational model, offers a potential framework for personalised disease management. This article discusses the development of a clinically grounded digital twin concept for PCOS that integrates endocrine, metabolic, behavioural and longitudinal outcome data to support clinician guided decision making. Potential applications are explored across lifestyle intervention, pharmacotherapy, fertility management and long term metabolic risk prediction. Key translational challenges, including data integration, model validation, ethical considerations, patient privacy and clinical implementation, are also discussed.
Conclusions
PCOS represents a compelling and underexplored area for digital twin development. By enabling dynamic modelling of individual disease trajectories and treatment responses, digital twins may support a transition from reactive symptom management towards anticipatory, personalised care. Further interdisciplinary research and clinical validation are required to realise the potential of this approach and facilitate its integration into routine PCOS management.