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COLLEGE PARK, Md. - A team of seven University of Maryland A. James Clark School of Engineering undergraduates earned the top prize in this year's National Institutes of Health (NIH) Design by Biomedical Undergraduate Teams (DEBUT) challenge for their efforts to develop low-cost tools to diagnose Alzheimer's disease before patients show symptoms.

"This represents a monumental achievement, not simply for the engineering community, but for the wider world of human health research," said Darryll J. Pines, dean of the Clark School and Nariman Farvardin Professor. "As rising sophomores, these seven students in many ways represent the future of biomedical innovation. Through collaborations with faculty and researchers across a range of disciplines, they have transformed ideas into innovation that could one day change how Alzheimer's and other diseases are diagnosed."

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The team, known as "Synapto," was awarded $20,000 from the NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) for developing a portable electroencephalogram (EEG) that uses a specially designed headset and a new software analysis tool to detect Alzheimer's disease before a patient displays clinical symptoms. The device exploits changes observed in the brainwaves of Alzheimer's patients in response to special auditory tones. The team, which recently filed for a company LLC in Maryland, believes their work could help make dementia diagnosis more quantitative, more systematic, and less costly - allowing doctors to use it at regular check-ups.

"Alzheimer's disease is the sixth leading cause of death in the United States, costing the nation close to $259 billion this year," said UMD Fischell Department of Bioengineering (BIOE) undergraduate and Synapto team captain Dhruv Patel, citing data from the Alzheimer's Association. "Diagnosing the disease early on allows patients to open up treatment options, manage the disease properly, and slow its progression."

Today, PET scans, MRIs, and spinal taps are most commonly used to diagnose Alzheimer's disease. Such methods are expensive and, at times, invasive, so many patients are diagnosed based only on their symptoms.

"It can take up to two years after clinical symptoms arise for patients to receive a proper diagnosis, and by then, he or she may have already seen significant progression of the disease," Patel said. "To address this, our technique allows us to characterize an Alzheimer's patient's brainwave using a variety of mathematical analytical tools and compare it with a healthy patient's brainwave to create a machine-learning model that can then accurately predict the probability of the patient having the disease."
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