Multi-Modal Deep Learning for Early Detection of Alzheimer’s Disease and Related Dementias

Multi-Modal Deep Learning for Early Detection of Alzheimer’s Disease and Related Dementias

 

Summary

 Alzheimer’s disease (AD) is a progressive neurodegenerative disease, and the most common cause of dementia with cognitive impairment in the elderly. An estimated 40 million people worldwide have AD and related dementias (ADRD), and this number is expected to triple by 2050 as the population ages, when the annual cost of dementia in the USA alone may exceed US$600 billion. While brain imaging including magnetic resonance imaging (MRI) and positron emission tomography (PET), cerebrospinal fluid (CSF) biomarkers, and serologic testing each may have diagnostic value, their high cost, invasiveness, and limited sensitivity and specificity, have forestalled their routine use in clinical practice. This team's long-term goal is to develop OCTA-based biomarkers for monitoring vascular contribution to various neurological diseases such as ADRD, Parkinson’s disease, multiple sclerosis, diabetic neuropathy, and optic neuropathy, the objective of this one-year project will be focused on the development of AI methods for early detection of ADRD using OCTA images. 

Team

Xiaodong Cai (Electrical & Computer Engineering), Jianhua Wang (Bascom Palmer Eye Institute), Liang Liang (Computer Science), Hong Jiang (Bascom Palmer Eye Institute)

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