EC Gynaecology

Pelvic floor disorders (PFDs)-including urinary incontinence, pelvic organ prolapse, chronic pelvic pain, and sequelae of obstetric trauma - remain among the most prevalent and functionally debilitating conditions in women’s health. Despite continuous advances in imaging, biomechanics, and urogynecologic surgery, clinical decision-making is too often constrained by fragmented data streams and cross-sectional “snapshots” of a dynamic, load-bearing, multi-tissue system. The emerging concept of the digital twin (DT) offers a tractable way forward: a continuously updated, computational representation of a system, informed by multimodal measurements and capable of simulating trajectories under alternative management strategies. In healthcare, digital twins have shown significant promise in managing chronic conditions, optimizing rehabilitation, and personalizing treatment [1-3]. A recent article lays out the foundational architecture for such a system in gynecology and urogynecology, motivating a shift from population-averaged heuristics to individualized, mechanistic care [4]. 

1. Mackey S., et al. “Digital twin learning health systems and multimodal biomarkers transform pain care”. Pain 11S (2025): S106-S110.
2. Saratkar SY., et al. “Digital twin for personalized medicine development”. Frontiers in Digital Health 7 (2025): 1583466.
3. Aghamiri SS., et al. “Digital twin technology in radiology”. Journal of Imaging Informatics in Medicine (2025).
4. Egorov V. “Digital twin of the female pelvic floor”. Open Journal of Obstetrics and Gynecology11 (2024): 1687-1694.
5. Egorov V. “Biomechanical mapping of the female pelvic floor”. 1^st Edition, Elsevier Academic Press (2023): 1-394.