Early Prediction of Alzheimer’s Disease Progression Using Variational Autoencoders

Prediction of Alzheimer’s disease before the onset of symptoms is an important clinical challenge, as it offers the potential for earlier intervention to interrupt disease progression before the development of dementia symptoms, as well as spur new prevention and treatment avenues. In this work, we propose a model that learns how to predict Alzheimer’s disease ahead of time from structural Magnetic Resonance Imaging (sMRI) data. The contributions of this work are two-fold: (i) We use the latent variables learned by our model to visualize areas of the brain, which contribute to confident decisions. Our model appears to be focusing on specific areas of the neocortex, cerebellum, and brainstem, which are known to be clinically relevant. (ii) There are various ways in which disease might evolve from a patient’s current physiological state. We can leverage the latent variables in our model to capture the uncertainty over possible future patient outcomes. It can help identify and closely monitor people who are at a higher risk of disease, despite the current lack of clinical indications.