Machine Learning for Predicting Alzheimer's Disease Progression from Brain Imaging
Authors
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Tim Rogers
Global Dynamics Corp.
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Vince Reed
Pinnacle College
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Will Cook
Horizon Labs
https://orcid.org/0009-0000-0000-0000
Abstract
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.
Copyright (c) 2025 Tim Rogers, Vince Reed, Will Cook (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.