END-TO-END 3D-CONVOLUTIONAL NEURAL NETWORK FOR PREDICTING CONVERSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DEMENTIA

Jinhyeong Bae, Jane Stocks, Ashley Heywood, Youngmoon Jung, Karteek Popuri, Mirza Faisal Beg, Lei Wang

Abstract:

Background
Predicting conversion to Alzheimer's Dementia (AD) among Mild Cognitive Impairment (MCI) patients is invaluable for patient care, as well as in selection for clinical trials. This project utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to develop end-to-end 3D-Convolutional Neural Network (3D-CNN) models to classify subjects who did not progress to AD (i.e., stable MCI or sMCI) vs. subjects who did progress to AD (i.e., progressive MCI or pMCI).