ArticleInternational neurourology journal2026
Bladder Digital Twins for Neuromodulation: Current Platforms and a Roadmap to Closed-Loop Therapy.
Article in International neurourology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Bladder disorders such as overactive bladder and neurogenic bladder impose a major symptom burden, yet neuromodulation therapies are largely delivered with fixed open-loop settings and limited adaptation to time-varying bladder states. Bladder digital twins are patient-anchored computational models linked to sensing streams and updated via state estimation and/or data assimilation. They offer a pathway to closed-loop personalized therapy, but bladder implementations remain fragmented across modeling, sensing, and stimulation. This review synthesizes digital-twin technologies relevant to bladder physiology, sensing, and neuromodulation and translates lessons from mature organ digital twins to bladder requirements for closed-loop control. We conducted targeted searches of PubMed, Web of Science, IEEE Xplore, and Google Scholar (2013-2025). We reviewed digital-twin paradigms for the heart, brain, lungs, liver, and kidney and summarized bladder approaches including finite-element bladder-wall mechanics, conductivity-based torso models for wearable bioimpedance optimization, strain-to-geometry reconstruction using stretchable sensors, and sensor-informed closed-loop control. Clinically deployable twins pair mechanistic structure with limited reliable data streams and use hybrid estimation for indirect, noisy measurements. In bladder applications, feasibility has been demonstrated for state estimation and model-guided design; key barriers include ambulatory motion artifacts and impedance drift, sparse clinical anchors for personalization, limited longitudinal human validation, and incomplete safety engineering for stimulation control. A roadmap toward closed-loop bladder digital twins should prioritize ambulatory-grade sensing with artifact handling and recalibration, uncertainty-aware real-time state estimation, modelinformed stimulation optimization, and fail-safe control with standardized reporting of accuracy, latency, uptime, and drift.
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