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Bladder Digital Twins for Neuromodulation: Current Platforms and a Roadmap to Closed-Loop Therapy.

Thar Htet Nyan¹, Suhyeok Kim¹, Eunkyoung Park²

¹Department of Biomedical Engineering, Soonchunhyang University, Asan, Korea.

International Neurourology Journal

|June 29, 2026

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Digital twins for bladder disorders offer personalized neuromodulation. This review synthesizes technologies and outlines a roadmap for closed-loop bladder digital twins, addressing sensing and control challenges for improved patient therapy.

Area of Science:

Biomedical Engineering
Computational Physiology
Digital Health

Background:

Bladder disorders like overactive bladder cause significant symptoms.
Current neuromodulation therapies use fixed settings, lacking adaptation to changing bladder states.
Bladder digital twins, computational models linked to sensing, promise personalized, closed-loop therapy.

Purpose of the Study:

To review digital twin technologies for bladder physiology, sensing, and neuromodulation.
To translate lessons from mature organ digital twins to bladder requirements for closed-loop control.
To identify barriers and propose a roadmap for developing closed-loop bladder digital twins.

Main Methods:

Conducted targeted literature searches (PubMed, Web of Science, IEEE Xplore, Google Scholar, 2013-2025).

Keywords:

Computer Simulation Electrical Stimulation Feedback Patient-Specific Modeling Physiologic Physiological; Monitoring Urinary Bladder

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Published on: November 28, 2019

Reviewed digital twin paradigms for heart, brain, lungs, liver, and kidney.

Summarized bladder-specific approaches including finite-element mechanics, bioimpedance, stretchable sensors, and closed-loop control.

Main Results:

Digital twins integrate mechanistic models with data streams for state estimation.
Bladder applications show feasibility for state estimation and model-guided design.
Key barriers include motion artifacts, impedance drift, sparse personalization data, limited validation, and safety engineering.

Conclusions:

A roadmap for closed-loop bladder digital twins requires ambulatory-grade sensing, artifact handling, and recalibration.
Prioritize uncertainty-aware state estimation, model-informed stimulation optimization, and fail-safe control.
Standardized reporting of accuracy, latency, uptime, and drift is crucial for clinical deployment.