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Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease
Darya N. Makarushkina, Vali A. Mamedov, Galiya Smagulova, Aynaza I. Khasanova, Nurgiza F. Yunusova, Adam R. Dadaev, Liliya K. Gabbasova, Aigul I. Sadykova et al.. Neurology Bulletin, 2025-12-16
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer disease represents a major medical and social challenge owning to the complexity of its diagnosis, especially at the preclinical stage. This underscores the need for noninvasive and cost-effective screening methods. This review summarizes current evidence on using ophthalmic imaging modalities and artificial intelligence technologies for early detection of Alzheimer disease. The diagnostic potential of retinal biomarkers identified with optical coherence tomography and optical coherence tomography angiography is detailed, including thinning of the peripapillary retinal nerve fiber layer, reduced macular and choroidal thickness, decreased capillary perfusion density, and deposition of pathological proteins. These changes correlate with cerebral condition and can be detected at preclinical stages of Alzheimer disease. The article describes the roles of machine-learning algorithms and neural networks in automated image analysis, demonstrating their ability to identify complex imaging patterns and substantially improve diagnostic accuracy (AUC 0.9). It also addresses methodological limitations and implementation challenges—including variability of results, insufficient specificity, and the black-box nature of artificial intelligence. We also highlight the high potential of multimodal approaches that combine retinal imaging with MRI and positron emission tomography. The evidence presented supports the feasibility of developing standardized protocols for the use of retinal biomarkers and artificial intelligence technologies as tools for large-scale screening of at-risk populations, enabling their integration into clinical practice for earlier initiation of therapy.
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APA 7th
Makarushkina, D. N., Mamedov, V. A., Smagulova, G., Khasanova, A. I., Yunusova, N. F., Dadaev, A. R., et al. (2025). Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease. Neurology Bulletin. https://doi.org/10.17816/nb690471
Vancouver
Makarushkina DN, Mamedov VA, Smagulova G, Khasanova AI, Yunusova NF, Dadaev AR, et al. Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease. Neurology Bulletin. 2025. doi:10.17816/nb690471.
BibTeX
@article{makarushkina2025-da942c04,
author = {Makarushkina, Darya N. and Mamedov, Vali A. and Smagulova, Galiya and Khasanova, Aynaza I. and Yunusova, Nurgiza F. and Dadaev, Adam R. and Gabbasova, Liliya K. and Sadykova, Aigul I. and others},
title = {{Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease}},
journal = {Neurology Bulletin},
year = {2025},
doi = {10.17816/nb690471},
url = {https://doi.org/10.17816/nb690471}
}
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Key findings
- It also addresses methodological limitations and implementation challenges—including variability of results, insufficient specificity, and the black-box nature of artificial intelligence.
Why it matters
This review summarizes evidence that ophthalmic imaging and artificial intelligence can help detect Alzheimer's disease at preclinical stages, with reported diagnostic accuracy (AUC 0.9). It also notes limitations including variability of results, insufficient specificity, and the black-box nature of artificial intelligence, and it does not address treatment, cure, or prevention.
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Study details
- Effect sizeAUC 0.9
- Evidence strengthLow
How this study scored 27/100
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. We have no data on senior author's track record, so that was left out: it earned 26 of the 96 points we could judge it on, which is 27 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Neurology Bulletin) |
| Journal's citation record | 0 / 4 | Neurology Bulletin: journal h-index 7 (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 | 4 / 5 | 2025 — 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 0 / 6 | Cited 0.0× 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 | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Daniil E. Ayupov holds only 1 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Bashkir State Medical University: institution h-index 76 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 26 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 26 of the 96 points we could judge it on is 27 out of 100. |
| Total | 27 / 100 | — |
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