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Modular machine learning for Alzheimer's disease classification from retinal vasculature
Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, Xiong S, Fang R.. Scientific reports, 2021-01-08
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
Key findings
- The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
Why it matters
This study used machine learning on retinal images from the UK Biobank to classify Alzheimer's disease with an average accuracy of 82.44%, and found that small blood vessels in the retina carry more information for diagnosis. It is about using eye images for diagnosis, not about treating, curing or preventing Alzheimer's disease.
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Study details
- Effect size82.44%
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, 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 | 3 / 5 | 2021 — within 6 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 5 / 7 | Cited 123 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.6 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 4 / 6 | Cited 11.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 3 / 4 | Cited 53 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ruogu Fang: h-index 29 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Florida: institution h-index 1,303 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33420208) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-020-80312-2
- PubMed: 33420208
- 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 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.
APA 7th
Tian, J., Smith, G., Guo, H., Liu, B., Pan, Z., Wang, Z., et al. (2021). Modular machine learning for Alzheimer's disease classification from retinal vasculature. Scientific reports. https://doi.org/10.1038/s41598-020-80312-2
Vancouver
Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, et al. Modular machine learning for Alzheimer's disease classification from retinal vasculature. Scientific reports. 2021. doi:10.1038/s41598-020-80312-2. PMID: 33420208.
BibTeX
@article{tian2021-33420208,
author = {Tian, J. and Smith, G. and Guo, H. and Liu, B. and Pan, Z. and Wang, Z. and Xiong, S. and Fang, R.},
title = {{Modular machine learning for Alzheimer's disease classification from retinal vasculature}},
journal = {Scientific reports},
year = {2021},
doi = {10.1038/s41598-020-80312-2},
pmid = {33420208},
url = {https://doi.org/10.1038/s41598-020-80312-2}
}
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