Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.

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Abstract

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and are unsuitable for continuous use. Here, we present a conformable wearable ultrasound system that combines lens-assisted acoustic focusing with machine-learning regression to enable non-invasive bladder volume estimation, while providing a clear path toward future real-time implementation. A flexible PZT array integrated with a concave acoustic lens enhances lateral energy concentration and depth selectivity, while a Random Forest model was used to map echo-derived features to bladder volume estimates. In a pilot study, bladder-volume estimates generated offline after data collection showed good agreement with a benchtop electrical impedance-based measurement system, supporting the feasibility of non-invasive bladder volume estimation. The device was operated using conservative low-voltage, low-duty-cycle excitation settings designed to minimize acoustic exposure and be consistent with diagnostic-ultrasound safety guidance, and biocompatible, flexible encapsulation is designed to support extended wear. Together with compact packaging and low-power wireless transmission, these attributes support ambulatory, longitudinal bladder monitoring and offer design insights for future wearable ultrasound systems targeting precise and ultimately continuous physiological monitoring.

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