UTA engineer will use deep learning tools to pinpoint types of dementia associated with Alzheimer's disease

UTA engineer will use deep learning tools to pinpoint types of dementia associated with Alzheimer's disease

A computer engineer at the University of Texas at Arlington will further develop and integrate powerful deep learning methods and tools to pinpoint types of dementias associated with Alzheimer's disease (ADRD), which in turn could help the medical community better treat these diseases.

Dajiang Zhu, assistant professor in the Department of Computer Science and Engineering, will lead a five-year, $2.86 million project supported by the National Institute of Neurological Disorders and Stroke (NINDS). Zhu will work with researchers at the University of North Carolina–Chapel Hill and the University of Georgia to focus on developing a deep learning model for ADRD analysis.

As the two most common types of dementia, Alzheimer's disease and Lewy body dementia (LBD) account for 65% to 85% of people with dementia nationwide, or about 7.5 million people.

Zhu said there are important differences in determining whether a patient has Alzheimer's or LBD. These differences can greatly influence the type of treatment they are prescribed. However, distinguishing between Alzheimer's disease and LBD is challenging due to both mixed pathologies and clinical symptoms.

"In this project, we will discover, define and represent individual GyralNets - a computational model that integrates both deep learning methods and neuroimaging markers - to characterize the abnormalities associated with Alzheimer's/LBD for individual patients,"

Dajiang Zhu, Assistant Professor, Department of Computer Science and Engineering, University of Texas at Arlington

He added that the project will ultimately compile, image and analyze large-scale brain data for practical clinical settings.

"Ultimately, we want to characterize and summarize deep relationships within the brain that will lead to improving the predictive ability between Alzheimer's and LBD," Zhu said. “We believe that earlier identification of what specific disease is present can lead to better outcomes through better treatment of these patients.”

Hong Jiang, Wendell H. Nedderman Endowed Professor and chair of the Department of Computer Science and Engineering, said Zhu's research has the potential for significant impact.

“To take all the data that can be collected and use it to help society and the people suffering from these diseases is monumental,” Jiang said. “It represents what university research is all about.”

Source:

University of Texas at Arlington

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