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2020 Alzheimer's Association Research Grant to Promote Diversity (AARG-D)

Artificial Intelligence to Improve Diagnosis of AD and Related Dementias

Can sophisticated software techniques help improve the accuracy of Alzheimer’s diagnosis?

Otto Pedraza, Ph.D.
Mayo Clinic Jacksonville
Jacksonville, FL - United States


Scientists are working to create innovative tools and strategies to improve the accuracy of Alzheimer’s diagnosis, which may also pave the way for effective treatments. An advanced computer science technique called “machine learning” is an emerging approach to find an optimal quantitative solution to a set of complex problems that involve large volumes of data. Dr. Otto Pedraza believes that using this novel approach may help improve the diagnosis of Alzheimer’s and all other dementia.

Research Plan

Dr. Pedraza and colleagues will leverage data from 454 individuals with dementia based on their clinical visits to the Mayo Clinic Jacksonville. The researchers will first study the data (from the individual’s initial visit to the clinic) which includes age, gender, years of education, clinical characteristics, cognitive test scores and brain scans as well as specific genetic information. Using these data, Dr. Pedraza’s team will employ machine learning and advanced statistical techniques to create an algorithm. This is aimed at improving the accuracy of early diagnosis of Alzheimer’s or Lewy Body Dementia (LBD). Next, the researchers will further analyze data from two subsequent annual clinic visits and evaluate whether their algorithm is effective at increasing the accuracy of diagnosis of the disease using information from all the 3 visits.


If successful, the study results may identify a novel technology to improve the accuracy of an early diagnosis of Alzheimer’s. Families facing Alzheimer’s now and in the future will benefit greatly from early detection, allowing for important care and planning. Furthermore, when we have new therapies, we will be in a better position to know who needs treatment at the earliest time point.

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