Predicting Neural Deterioration in Patients with Alzheimer's Disease Using a Convolutional Neural Network.

Alzheimer's disease causes neural damage, including brain atrophy in the patient. Consequently, ventricles that contain cerebral fluid a re expanded to filling those regions, which increases the proportional volume of ventricles in the brain. Therefore, abnormal growth of ventricle volume is an important indicator for estimating neural damage and, in turn, for the progression of Alzheimer's diseases. The rate of this volume-growth, i.e., neural damage, can be predicted by predictive and machine learning models using the patient's current status. These predictions help assess the effectiveness of a particular treatment for a patient, in addition to providing some expectation of the disease timeline. In this work, we propose a convolutional neural network (CNN) model using region-level features for predicting ventricle volume biomarkers. The region-level representation with domain-driven features benefits from the CNN spatial pattern recognition capability. It also prevents learning irrelevant features and overfitting to the training data as a result of data scarcity. Our model is applied to the ADNI dataset in the TADPOLE competition and outperforms the best leaderboard results.