For effective treatment of Alzheimer disease (AD), it is important to
identify subjects who are most likely to exhibit rapid cognitive decline.
Herein, we developed a novel framework based on a deep convolutional neural
network which can predict future cognitive decline in mild cognitive impairment
(MCI) patients using flurodeoxyglucose and florbetapir positron emission
tomography (PET). The architecture of the network only relies on baseline PET
studies of AD and normal subjects as the training dataset. Feature extraction
and complicated image preprocessing including nonlinear warping are unnecessary
for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI
patients outperformed conventional feature-based quantification approaches. ROC
analyses revealed that performance of CNN-based approach was significantly
higher than that of the conventional quantification methods (p < 0.05). Output
scores of the network were strongly correlated with the longitudinal change in
cognitive measurements. These results show the feasibility of deep learning as
a tool for predicting disease outcome using brain images.