Seminar | April 30 | 12:30-1:30 p.m. | 521 Cory Hall
Millions of patients suffer from the consequences of spinal cord injury (SCI) and congenital spinal anomalies. Although many of these patients have obvious limitations in mobility, unbeknownst to the general public is that nearly all have neurogenic bladder dysfunction and lack control of their bladder. Since SCI patients are unable to sense bladder fullness, they are recommended to catheterize every 2 to 4 hours throughout the day. This high frequency of emptying adds insult to injury. A common problem is making the trip to the bathroom and only finding a small amount of urine in the bladder. Or worse, not getting to the bathroom in time and leaking because the bladder was too full.
To address this problem, we aim to build a non-invasive, patch-like device that would be worn by SCI patients to receive timely alerts for starting to look for a bathroom to perform catheterization. The device would utilize an array of LEDs and photodetectors to infer spatial expansion of the bladder. The underlying physical principle exploited by our device is measurement of back scattered light at wavelengths for which water has high absorption coefficient (e.g., ~950nm) via an array of light source and detectors with fixed distances. We will develop machine learning algorithms to identify patterns in light absorption maps generated by the sensor array, and to personalize the alert to better match individual patient's body characteristics and preferences. Extensive empirical studies with bladder replicas, swine bladder and healthy human volunteers will be carried out.
A number of modalities have been evaluated for predicting bladder volume. Unfortunately, none have proven reliable and as expected, the more invasive applications that require implantation of sensors have the greatest validity and reliability. Our current biosensor uses near-infrared spectroscopy and has shown promising results ex vivo but may have limited utility in vivo due to human body habitus.
My goal is to present the clinical problem to engineers with expertise in sensors and have a back-and-forth discussion on potential solutions and their limitations, ex vivo and in vivo testing in animal models and patients.
Faculty, Staff, Students - Graduate
RSVP online by April 29.