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Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review
Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY.. Ophthalmology and therapy, 2024-06-24
Topic: Risk, prevention and care in population studies
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
Key findings
- We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease).
- There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis.
Why it matters
This systematic review found that AI algorithms applied to retinal fundus photographs can identify several disorders, including dementia, with high accuracy. It is about screening and diagnosis, not about treating, curing or preventing Alzheimer's disease.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 65/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Ophthalmology and therapy) |
| Journal's citation record | 0 / 4 | Ophthalmology and Therapy: journal h-index 62 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 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 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 100 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 25.0 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 8.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 100 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 | 1 / 3 | Tsinghua University: institution h-index 1,218 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38913289) |
| Free to read | 2 / 2 | Free to read |
| Total | 65 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1007/s40123-024-00981-4
- PubMed: 38913289
- Original source →
- Free full text: Ophthalmology and Therapy →
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 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
Grzybowski, A., Jin, K., Zhou, J., Pan, X., Wang, M., Ye, J., et al. (2024). Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmology and therapy. https://doi.org/10.1007/s40123-024-00981-4
Vancouver
Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, et al. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmology and therapy. 2024. doi:10.1007/s40123-024-00981-4. PMID: 38913289.
BibTeX
@article{grzybowski2024-38913289,
author = {Grzybowski, A. and Jin, K. and Zhou, J. and Pan, X. and Wang, M. and Ye, J. and Wong, T. Y.},
title = {{Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review}},
journal = {Ophthalmology and therapy},
year = {2024},
doi = {10.1007/s40123-024-00981-4},
pmid = {38913289},
url = {https://doi.org/10.1007/s40123-024-00981-4}
}
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