An Alzheimer's Disease Progression Score Using Supervised Variational Autoencoders on MRI Anatomic Imaging Data

Motivation: Alzheimer's disease affects millions, but understanding its progression remains challenging. This study seeks to assess the severity of Alzheimer's from imaging data alone. Goal(s): To create a score that reflects how far Alzheimer's has progressed in a patient. Approach: Using brain scans and simple patient demographic information, we developed an imaging-based model that predicts the severity of Alzheimer's. Results: Our model successfully distinguishes between different stages of Alzheimer's, offering a reliable disease progression score. Impact: This work could lead to earlier detection and better tracking of Alzheimer's, informing treatment decisions and aiding in the objective development and evaluation of new therapies.