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November 9, 2020

Yin is part of team using machine learning to map nerves and restore bladder function

Written By: Jason Daley

Chemical and Biological Engineering Vilas Distinguished Achievement Professor John Yin is part of a multi-institution grant from the National Institutes of Health’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program.

Image of bladder research

The goal of the project is to create a computational model using machine learning that can effectively identify nerves that control the bladder and guide the development of new therapeutic electrical signal stimulations.

The nervous system is extremely complicated and identifying exactly what each individual nerve controls is a matter of trial and error using current techniques. Mathematically modeling the complex network of nerve connections is very difficult, and it’s hard to predict which nerves interact with one another.

The team plans to apply machine learning techniques to more accurately map out the nerve network in the urinary tract and identify the connections and functions of the nerves.

Currently, bladder dysfunction affects about 50 percent of women and 25 percent of men. The hope is that the modeling will guide the development of new therapeutic electrical signal stimulations that can help control the bladder using an external device.

By the end of the two-year, $1 million grant, the team hopes to have developed a new modeling framework that can help emulate entire organ systems.

Collaborators include Zachary Danziger of Florida International University, Giovanna Guidobon of the University of Missouri, Deniz Erdogmus and Sumientra Rampersad from Northeastern University and Elie Alhajjar from the United States Military Academy.

All of the researchers involved met virtually through NIH’s 2020 SPARC Ideas Lab, a four-day conference inviting researchers to discuss bioelectronic medicine.

Yin says the virtual nature of the collaboration is almost as significant as the research itself. “An exciting and unprecedented feature is that our collaboration was conceived and is being carried out entirely online,” he says. “We are part of a big experiment that points toward the future of collaborative science and engineering.”