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Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI
Dukart J, Mueller K, Barthel H, Villringer A, Sabri O, Schroeter ML, Alzheimer's Disease Neuroimaging Initiative.. Psychiatry research, 2012-11-10
Topic: Amyloid, tau and blood-test diagnosis
The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes reveal
Key findings
- To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort.
- SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification.
- For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained.
- Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%.
Why it matters
This study used a machine learning method with MRI and FDG-PET scans to detect Alzheimer's disease across different clinical centers. It is a diagnostic approach and does not provide a treatment, cure, or prevention.
Study details
- Study designmeta-analysis
- Participants21
- Evidence strengthHigh
How this study scored 65/100
Published in a peer-reviewed journal, and it is a meta-analysis pooling many studies. Scored zero for effect reported because the record does not state it. We have no data on journal's citation record, so that was left out: it earned 62 of the 96 points we could judge it on, which is 65 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Psychiatry research) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A meta-analysis pooling many studies |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2013 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 21 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 130 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.7 citations a year over 15 year(s) since 2013 |
| Impact for its field and year | 3 / 6 | Cited 4.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 8 other studies in this library |
| Still being cited | 2 / 4 | Cited 19 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Matthias Leopold Schroeter: h-index 71 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Lausanne: institution h-index 994 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 23149027) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 62 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 62 of the 96 points we could judge it on is 65 out of 100. |
| Total | 65 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Dukart, J., Mueller, K., Barthel, H., Villringer, A., Sabri, O., Schroeter, M. L., et al. (2013). Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI. Psychiatry research. https://doi.org/10.1016/j.pscychresns.2012.04.007
Vancouver
Dukart J, Mueller K, Barthel H, Villringer A, Sabri O, Schroeter ML, et al. Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI. Psychiatry research. 2013. doi:10.1016/j.pscychresns.2012.04.007. PMID: 23149027.
BibTeX
@article{dukart2013-23149027,
author = {Dukart, J. and Mueller, K. and Barthel, H. and Villringer, A. and Sabri, O. and Schroeter, M. L. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI}},
journal = {Psychiatry research},
year = {2013},
doi = {10.1016/j.pscychresns.2012.04.007},
pmid = {23149027},
url = {https://doi.org/10.1016/j.pscychresns.2012.04.007}
}
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