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Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia
Katako A, Shelton P, Goertzen AL, Levin D, Bybel B, Aljuaid M, Yoon HJ, Kang DY et al.. Scientific reports, 2018-09-05
Topic: Amyloid, tau and blood-test diagnosis
Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.
Key findings
- General linear model, scaled subprofile modeling and support vector machines were examined.
- Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation.
- We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S.
- Korea) and Asan Medical Centre (Seoul, S.
- Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.
Why it matters
A machine learning classification algorithm using FDG-PET distinguished Alzheimer's disease from normal subjects and was also sensitive to Parkinson's disease dementia and dementia with Lewy bodies. This is a diagnostic finding, not a treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 44/100
Published in a peer-reviewed 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 | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 2 / 5 | 2018 — within 10 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 | 4 / 7 | Cited 76 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.6 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 4.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 25 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ji Hyun Ko: h-index 35 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Manitoba: institution h-index 666 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30185806) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 100 | — |
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Read the original
- DOI: 10.1038/s41598-018-31653-6
- PubMed: 30185806
- Original source →
- Free full text: Scientific Reports →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Katako, A., Shelton, P., Goertzen, A. L., Levin, D., Bybel, B., Aljuaid, M., et al. (2018). Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Scientific reports. https://doi.org/10.1038/s41598-018-31653-6
Vancouver
Katako A, Shelton P, Goertzen AL, Levin D, Bybel B, Aljuaid M, et al. Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Scientific reports. 2018. doi:10.1038/s41598-018-31653-6. PMID: 30185806.
BibTeX
@article{katako2018-30185806,
author = {Katako, A. and Shelton, P. and Goertzen, A. L. and Levin, D. and Bybel, B. and Aljuaid, M. and Yoon, H. J. and Kang, D. Y. and others},
title = {{Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia}},
journal = {Scientific reports},
year = {2018},
doi = {10.1038/s41598-018-31653-6},
pmid = {30185806},
url = {https://doi.org/10.1038/s41598-018-31653-6}
}
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