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Automatic classification of MR scans in Alzheimer's disease
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR et al.. Brain : a journal of neurology, 2008-01-17
Topic: Amyloid, tau and blood-test diagnosis
To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.
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APA 7th
Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., et al. (2008). Automatic classification of MR scans in Alzheimer's disease. Brain : a journal of neurology. https://doi.org/10.1093/brain/awm319
Vancouver
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, et al. Automatic classification of MR scans in Alzheimer's disease. Brain : a journal of neurology. 2008. doi:10.1093/brain/awm319. PMID: 18202106.
BibTeX
@article{kloppel2008-18202106,
author = {Klöppel, S. and Stonnington, C. M. and Chu, C. and Draganski, B. and Scahill, R. I. and Rohrer, J. D. and Fox, N. C. and Jack, C. R. and others},
title = {{Automatic classification of MR scans in Alzheimer's disease}},
journal = {Brain : a journal of neurology},
year = {2008},
doi = {10.1093/brain/awm319},
pmid = {18202106},
url = {https://doi.org/10.1093/brain/awm319}
}
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Key findings
- Data from different centres were successfully combined achieving comparable results from the separate analyses.
Why it matters
This 2008 study trained a computer method (linear support vector machines) on grey-matter MRI scans and correctly classified "up to 96% of pathologically verified AD patients" versus cognitively normal older people, and 89% of people with mild, clinically probable Alzheimer's versus age/sex-matched controls. A classifier trained on one centre's scans also separated Alzheimer's from normal ageing on another centre's scanner, and the method assigned 89% of autopsy-confirmed cases to Alzheimer's or frontotemporal lobar degeneration. The record gives no group sizes, and it is a diagnostic-method study, not a treatment or prevention finding.
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Study details
- Evidence strengthLow
How this study scored 60/100
Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Brain : a journal of neurology) |
| Journal's citation record | 4 / 4 | Brain: journal h-index 503 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2008 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 7 / 7 | Cited 1,178 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 58.9 citations a year over 20 year(s) since 2008 |
| Impact for its field and year | 6 / 6 | Cited 44.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 4 / 6 | Cited by 50 other studies in this library |
| Still being cited | 3 / 4 | Cited 115 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Richard S. J. Frackowiak: h-index 188 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 18202106) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 60 / 100 | — |
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