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C.8 BladTrack — A Wearable Ultrasound Patch for Continuous Bladder Volume Monitoring

F25 · August 18, 2025

IP Requirement: Emory IP

Experience Requirement:

– Mechanical Design

– Rapid Prototyping

Problem Description

Urinary retention and incontinence are major challenges in both inpatient and outpatient care, particularly among elderly, post-operative, and neurologically impaired patients. An estimated 50% of hospitalized patients experience some form of bladder dysfunction, with up to 80% of patients with spinal cord injury developing neurogenic bladder, or an inability of your central nervous system (CNS) to communicate with your urinary system. Inadequate monitoring can lead to complications such as urinary tract infections (UTIs), kidney damage, or foley catheter dependence. Inside the hospital, bladder volume monitoring in performed by nursing staff using a specific bladder ultrasound but remains partially subjective and user-dependent, leading to unnecessary catheterizations. Outside of the hospital, there are no current methods of tracking bladder distention in at-risk patient populations and remains a clinical diagnosis, which often can lead to severe patient complications at home and in nursing facilities. 

Currently, bladder volume is assessed through manual palpation, handheld ultrasound scans, or via indwelling catheters, which carry risk of infection, such as CAUTIs (Catheter-Associated Urinary Tract Infections). These tools are operator-dependent and invasive. Currently, there is no wearable, non-invasive, and real-time solution to continuously monitor bladder filling, particularly one suited for mobile patients or at-home use. 

This researcher proposes a skin-adherent ultrasound patch capable of continuous, real-time bladder volume monitoring. This technology could be composed of low-frequency transducers embedded in a stretchable silicone patch that detects the bladder wall and fluid interface. Ideally, the echo data would be processed locally or transmitted to a mobile device for volume estimation using a machine learning algorithm. The device would provide caregivers with live bladder filling trends, alerts when volumes approach risk thresholds (>400 mL), and allows for non-invasive monitoring without infection risk. This technology could reduce unnecessary catheterizations, improve continence care, and enhance quality of life for millions of patients.

Filed Under: F25

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