Abstract #1868

An end-to-end MR-based classification of arteriolar sclerosis using 3D convolutional neural networks

Nazanin Makkinejad¹, Ashish A. Tamhane², Carles Javierre Petit¹, Arnold M. Evia², David A. Bennett², Julie A. Schneider², and Konstantinos Arfanakis^1,2

¹Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, ²Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States

Arteriolar sclerosis is common in the brains of older adults and has been shown to be associated with cognitive decline and dementia. Definitive diagnosis of arteriolar sclerosis is only possible at autopsy. The purpose of this work was to develop an end-to-end deep learning model to predict the presence of severe arteriolar sclerosis from MR images without the need to extract hand-engineered features. The model was developed by combining ex-vivo MRI and pathology data in a large community-based cohort of older adults.

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