Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging
This addresses the problem of early identification of high-risk Alzheimer's patients for clinicians, but it is incremental as it applies an existing deep learning method to a specific medical imaging task.
The researchers tackled predicting rapid cognitive decline in mild cognitive impairment patients using deep learning on brain PET scans, achieving 84.2% accuracy in predicting conversion to Alzheimer's disease, which outperformed conventional methods.
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.