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A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Hilal S, Venketasubramanian N, Cheng CY et al.. The Lancet. Digital health, 2022-09-30
Topic: Amyloid, tau and blood-test diagnosis
We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]).
Key findings
- We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative).
- In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]).
Why it matters
This retrospective case-control study tested a deep learning model using retinal photographs to detect Alzheimer's disease, with improved performance in patients with eye disease or diabetes. It is a diagnostic study and does not address treating, curing, or preventing Alzheimer's disease.
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Study details
- Study designcase-control study
- Participants648
- Evidence strengthModerate
How this study scored 67/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (The Lancet. Digital health) |
| Journal's citation record | 1 / 4 | The Lancet Digital Health: journal h-index 132 (OpenAlex, 2026-10-29) |
| Study design | 6 / 12 | A case-control study |
| 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 | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 648 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 265 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 44.2 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 5 / 6 | Cited 26.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 11 other studies in this library |
| Still being cited | 4 / 4 | Cited 219 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Tien Yin Wong: h-index 207 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Mayo Clinic: institution h-index 1,606 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 5 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36192349) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 67 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/s2589-7500(22)00169-8
- PubMed: 36192349
- Original source →
- Free full text: The Lancet Digital Health →
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
Cheung, C. Y., Ran, A. R., Wang, S., Chan, V. T. T., Sham, K., Hilal, S., et al. (2022). A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. The Lancet. Digital health. https://doi.org/10.1016/s2589-7500(22)00169-8
Vancouver
Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Hilal S, et al. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. The Lancet. Digital health. 2022. doi:10.1016/s2589-7500(22)00169-8. PMID: 36192349.
BibTeX
@article{cheung2022-36192349,
author = {Cheung, C. Y. and Ran, A. R. and Wang, S. and Chan, V. T. T. and Sham, K. and Hilal, S. and Venketasubramanian, N. and Cheng, C. Y. and others},
title = {{A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study}},
journal = {The Lancet. Digital health},
year = {2022},
doi = {10.1016/s2589-7500(22)00169-8},
pmid = {36192349},
url = {https://doi.org/10.1016/s2589-7500(22)00169-8}
}
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