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Abstract #4707

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

Junhyoun Sung1, Dean Shibata2,3, Kwun Chuen Gary Chan1,3, Lan Shui3,4, and David Haynor2
1Department of Biostatistics, University of Washington, Seattle, WA, United States, 2Department of Radiology, University of Washington, Seattle, WA, United States, 3National Alzheimer’s Coordinating Center, Seattle, WA, United States, 4Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Diagnosis/Prediction, Alzheimer's Disease

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.

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