Estimating Alzheimer's Disease Progression Score Using Machine Learning on FreeSurfer‐Derived MRI Gray Matter Volumes
Junhyoun Sung, Dean Shibata, Kwun Chuen Gary Chan, Lan Shui, David R Haynor · 2025
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Description
Junhyoun Sung and co-authors build a score that tracks Alzheimer's disease progress from brain scans, in a 2025 study. The disease unfolds at uneven rates, which makes early diagnosis and follow-up hard. The team trained a machine learning model on gray matter volumes drawn from MRI scans. Age and sex joined the scan data as inputs. Two large study cohorts supplied close to a thousand scans in total.
The model sorted disease stages with near-perfect test scores when all scan features were used. Outer brain volumes predicted stage better than deep volumes did. A single progression score per person matched standard thinking tests well, rising as those scores worsened. The authors present the score as a number-based marker of decline. They say it could help doctors gauge where a patient stands without a long test battery.
Catalog record
| CREATOR | Junhyoun Sung, Dean Shibata, Kwun Chuen Gary Chan, Lan Shui, David R Haynor |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 3 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |