Among the award winners and finalists in the 2015 G. Steven Burrill Business Plan Competition, an annual student competition offered through the Wisconsin School of Business, were innovative entries submitted by biomedical engineering students. They included a device that could prevent bladder cancer recurrence and a computer program that helps diagnose patients with sleep apnea.
During existing processes of bladder cancer tumor removal, there is a 70-percent chance the tumor will recur. This is because the resectoscope, a transurethral device used to shave off parts of the tumor, cannot account for slices of the tumor seeding back into the bladder wall during or after the procedure.
ITR Medical, which won the second-place prize of $7,000, designed an improved resectoscope. It includes a negative pressure suction that stops tumor seeding by absorbing the free-floating tumor. This new device would reduce the need for further procedures or chemotherapy after the initial, early-stage bladder cancer treatments. According to biomedical engineering senior Katie Baldwin, the product would significantly reduce recurrence rates in bladder patients.
“The products in the current market don’t really have a solution to the tumor seeding that happens routinely in the early stages of bladder cancer,” Baldwin says. “Ours would be the first and only device on the market that would try to prevent that tumor seeding.”
The group, including biomedical engineering students Baldwin, Alyssa Mitchell, Tyler Moon and Ryan Reynebeau (Mitchell and Reynebeau recently graduated), is looking to submit an invention disclosure report to the Wisconsin Alumni Research Foundation (WARF) to boost its opportunities for product development. The students are facing a number of possibilities, but one of the group’s most fortuitous options looking forward is to pair with a larger medical manufacturing company.
EnsoData, another BME-related team involved in the Burrill competition, was one of four finalists. The group secured $500 for its software tool, called CloudClinic Enterprise.
The tool is based on a complex, machine-learning algorithm that can be trained to diagnose patients with sleep apnea. The system the team developed can learn, with very high accuracy, how sleep lab technicians would analyze or score sleep apnea signals. By doing this, the software can analyze the data more quickly and streamline the diagnostic process.
Biomedical engineering graduate student Chris Fernandez says that his group’s product can significantly cut the amount of time it takes for lab technicians to process information, and determine whether a patient has sleep apnea.
“Our objective is to bring tools to sleep labs that can make them more efficient, help them provide higher quality care even faster than they do now, so they can expand their reach and start treating this huge untapped patient population,” Fernandez says.
EnsoData is currently beta-testing its software with the Wisconsin Sleep Clinic. Fernandez anticipates that if the software can effectively process large amounts of data, his team may be able to apply it to other disorders as well. Other members of the group include Sam Rusk and Alex Whittow.