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

Area of Science:

• Biomedical Engineering
• Medical Devices
• Wearable Technology

Background:

• Continuous bladder volume monitoring is crucial for managing lower urinary tract dysfunctions.
• Existing methods are often invasive, operator-dependent, and unsuitable for long-term use.

Purpose of the Study:

• To develop a conformable wearable ultrasound system for non-invasive bladder volume estimation.
• To enable future real-time implementation for continuous physiological monitoring.

Main Methods:

• A flexible PZT array with a concave acoustic lens for enhanced acoustic focusing.
• Machine learning regression (Random Forest) to map ultrasound echo features to bladder volume.
• Low-voltage, low-duty-cycle excitation for safety and biocompatible encapsulation for extended wear.

Main Results:

• The wearable system demonstrated good agreement with a benchtop electrical impedance system in a pilot study.
• Offline bladder volume estimates validated the feasibility of the non-invasive approach.
• The system's design supports ambulatory and longitudinal bladder monitoring.

Conclusions:

• The developed wearable ultrasound system offers a feasible solution for non-invasive bladder volume monitoring.
• The technology supports continuous, ambulatory monitoring and provides insights for future wearable ultrasound devices.
• This approach has the potential to significantly improve the management of lower urinary tract dysfunctions.