Machine Learning for Predicting Alzheimer's Disease Progression from Brain Imaging

Identifying individuals at high risk of developing Alzheimer's disease (AD) before significant cognitive decline occurs is one of medicine's greatest challenges. This study utilizes a multi-modal machine learning approach to predict the progression from Mild Cognitive Impairment (MCI) to clinical AD. Our model integrates data from three distinct imaging modalities: structural MRI (to measure cortical atrophy), fMRI (to assess functional connectivity changes), and PET scans (to quantify amyloid-beta plaque deposition). By combining these diverse data streams, our model captures a holistic view of the disease's neurobiology. We trained a 3D CNN-based classifier on a longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model successfully predicted MCI-to-AD conversion within a 3-year window with an accuracy of 91.2%. These findings offer a promising, non-invasive tool for patient stratification in clinical trials, allowing new therapeutic interventions to be tested on the correct patient populations, and providing a crucial window for future preventative care.