Toward virtual bladder: real-time bladder volume monitoring with flexible AuCNT strain sensors

Abstract

Digital twin technology holds considerable potential for personalized diagnostics and treatment of bladder dysfunction, particularly neurogenic conditions such as underactive bladder (UAB). In this study, to address the need for precise monitoring, we introduce a flexible, stretchable strain sensor composed of gold-coated carbon nanotubes (AuCNTs) embedded in Ecoflex. We specifically designed a three-channel configuration to capture anisotropic expansion and evaluated the sensor's performance using both two-dimensional balloon models and ex-vivo three-dimensional porcine bladder models. As a result, the AuCNT sensor demonstrated high sensitivity, and the three-channel design significantly enhanced spatial accuracy compared to single-channel approaches. Based on these measurements, we created a preliminary "Virtual Bladder" model that provides dynamic, real-time visualization of bladder volume changes. While our current model requires further development to incorporate multimodal data and anatomical variability, it serves as a foundational step towards developing advanced digital twin frameworks and closed-loop neuromodulation systems for bladder dysfunction.