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ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data
Hao Jiang, Yishan Qian, Liqiang Zhang, Tao Jiang, Yonghang Tai. Frontiers in Public Health, 2025-01-03
Topic: Amyloid, tau and blood-test diagnosis
The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities. Our method achieved segmentation accuracies of 79.1% on the DRIVE dataset, 68.3% on the HRF dataset. Utilizing a multimodal dataset comprising both healthy and AD subjects, ReIU extracted vascular density from fundus images, facilitating primary AD screening with a classification accuracy of 79%. These results demonstrate ReIU's substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer's disease. This study underscores the importance of integrating multi-modal data and deep learning techniques in advancing the early detection and management of Alzheimer's disease.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Jiang, H., Qian, Y., Zhang, L., Jiang, T., & Tai, Y. (2025). ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2024.1449798
Vancouver
Jiang H, Qian Y, Zhang L, Jiang T, Tai Y. ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data. Frontiers in Public Health. 2025. doi:10.3389/fpubh.2024.1449798. PMID: 39830185.
BibTeX
@article{jiang2025-39830185,
author = {Jiang, Hao and Qian, Yishan and Zhang, Liqiang and Jiang, Tao and Tai, Yonghang},
title = {{ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data}},
journal = {Frontiers in Public Health},
year = {2025},
doi = {10.3389/fpubh.2024.1449798},
pmid = {39830185},
url = {https://doi.org/10.3389/fpubh.2024.1449798}
}
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Key findings
- The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs.
- These results demonstrate ReIU's substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer's disease.
Why it matters
This study used retinal vessel segmentation from eye imaging to screen for Alzheimer's disease with a classification accuracy of 79%. The authors describe it as a potential economical, non-invasive screening tool, but it is a preliminary framework and does not test treatment or prevention.
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Study details
- Effect size79.1%; 68.3%
- Evidence strengthLow
How this study scored 47/100
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 (Frontiers in Public Health) |
| Journal's citation record | 2 / 4 | Frontiers in Public Health: journal h-index 174 (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 | 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 | 3 / 7 | Cited 24 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 13.0× 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 | 2 / 4 | Cited 24 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Yonghang Tai: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Fudan University: institution h-index 954 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39830185) |
| Free to read | 2 / 2 | Free to read |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fpubh.2024.1449798
- PubMed: 39830185
- Original source →
- Free full text: Frontiers in Public Health →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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