EKU Computer Science Student Utilizes AI to Help Diagnose Alzheimer’s Disease Earlier

EKU Online > News & Updates > Computer Science > EKU Computer Science Student Utilizes AI to Help Diagnose Alzheimer’s Disease Earlier

Alzheimer’s disease is a chronic neurodegenerative disease, which causes progressive damage to patients’ ability in memory, language, behavior, and problem-solving. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer’s dementia today. In 2019, official death certificates recorded 121,499 deaths from Alzheimer’s disease in the U.S., and this trajectory of deaths from the disease was likely exacerbated in 2020 by the COVID-19 pandemic. No treatment available at this time can cure or completely stop the progression of Alzheimer’s disease. This is most likely due to a lack of clear understanding of the cause of the disease and the fact that Alzheimer’s disease cannot be easily identified at early stages. Early diagnosis is crucial for Alzheimer’s disease treatment and potential drug development.   

Qi Ying, an EKU computer science graduate student, proposed a novel method for Alzheimer’s disease early diagnosis through his CSC 890 — independent study course, under the supervision of Dr. Gongbo “Tony” Liang, whose research focuses on using modern machine learning and neural network techniques to solve real-world challenges. The proposed method uses advanced artificial neural network techniques to analyze patients’ brain magnetic resonance imaging (MRI) and single-nucleotide polymorphisms (SNPs) information from a Genome-wide association study (GWAS) simultaneously. This work is one of the earliest works using MRI and GWAS findings in combination for Alzheimer’s disease early diagnosis. The preliminary result suggests that the proposed method may achieve approximately 94 percent accuracy on early diagnosis of Alzheimer’s disease comparable to human-level performance (95 percent).  

Qi graduated from the MS in Computer Science program in Spring 2021.  He now works as a Medical Imaging Engineer Associate at the University of Iowa and continues to work on brain MRI analysis using modern machine learning and neural network techniques. A manuscript based on his CSC 890 work on Alzheimer’s disease early diagnosis has been accepted to the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’21).

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