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A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks
Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z.. Journal of medical systems, 2019-12-18
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases.
Key findings
- Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task.
- Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases.
- In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages.
- However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied.
Why it matters
This study developed a deep learning approach using resting-state fMRI to classify Alzheimer's disease stages. It is a diagnostic tool development study and does not test any treatment, cure, or prevention.
Study details
- Study designlongitudinal study
- Participants138
- Effect size96.85%; 97.38%; 97.43%
- Evidence strengthModerate
How this study scored 64/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of medical systems) |
| Journal's citation record | 1 / 4 | Journal of Medical Systems: journal h-index 143 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 138 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 6 / 7 | Cited 434 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 48.2 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 5 / 6 | Cited 23.2× 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 | 4 / 4 | Cited 201 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Zahid Mehmood: h-index 41 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Bahauddin Zakariya University: institution h-index 226 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31853655) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 64 / 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
Ramzan, F., Khan, M. U. G., Rehmat, A., Iqbal, S., Saba, T., Rehman, A., et al. (2019). A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks. Journal of medical systems. https://doi.org/10.1007/s10916-019-1475-2
Vancouver
Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, et al. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks. Journal of medical systems. 2019. doi:10.1007/s10916-019-1475-2. PMID: 31853655.
BibTeX
@article{ramzan2019-31853655,
author = {Ramzan, F. and Khan, M. U. G. and Rehmat, A. and Iqbal, S. and Saba, T. and Rehman, A. and Mehmood, Z.},
title = {{A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks}},
journal = {Journal of medical systems},
year = {2019},
doi = {10.1007/s10916-019-1475-2},
pmid = {31853655},
url = {https://doi.org/10.1007/s10916-019-1475-2}
}
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