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Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images
Gupta Y, Lee KH, Choi KY, Lee JJ, Kim BC, Kwon GR, National Research Center for Dementia, Alzheimer’s Disease Neuroimaging Initiative.. PloS one, 2019-10-04
Topic: Amyloid, tau and blood-test diagnosis
In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer's disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Gupta, Y., Lee, K. H., Choi, K. Y., Lee, J. J., Kim, B. C., Kwon, G. R., et al. (2019). Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images. PloS one. https://doi.org/10.1371/journal.pone.0222446
Vancouver
Gupta Y, Lee KH, Choi KY, Lee JJ, Kim BC, Kwon GR, et al. Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images. PloS one. 2019. doi:10.1371/journal.pone.0222446. PMID: 31584953.
BibTeX
@article{gupta2019-31584953,
author = {Gupta, Y. and Lee, K. H. and Choi, K. Y. and Lee, J. J. and Kim, B. C. and Kwon, G. R. and {National Research Center for Dementia} and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images}},
journal = {PloS one},
year = {2019},
doi = {10.1371/journal.pone.0222446},
pmid = {31584953},
url = {https://doi.org/10.1371/journal.pone.0222446}
}
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 8 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.
Key findings
- These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low.
- However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects.
- In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals.
- The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques.
Why it matters
This is a technical study proposing a new computer method to classify brain MRI scans as Alzheimer's, mild cognitive impairment, or healthy. It is a diagnostic tool for research and does not test any treatment, cure, or prevention.
Study details
- Participants326
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 49 of the 96 points we could judge it on, which is 51 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PloS one) |
| Journal's citation record | 4 / 4 | PLoS ONE: journal h-index 594 (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 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 326 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 107 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 7.2× 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 45 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | Chonnam National University: institution h-index 438 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31584953) |
| Free to read | 2 / 2 | Free to read |
| Points | 49 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 49 of the 96 points we could judge it on is 51 out of 100. |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
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