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Color Fundus Photography and Deep Learning Applications in Alzheimer Disease
Oana M Dumitrascu, Xin Li, Wenhui Zhu, Bryan K Woodruff, Simona Nikolova, Jacob Sobczak, Amal Youssef, Siddhant Saxena et al.. Mayo Clinic proceedings. Digital health, 2024-08-26
Topic: Brain imaging, connectivity and assessment
The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P<.001) and our institutional testing data sets (98.90% vs 94.17%; P=.04). No major differences were noted between the original and binary vessel segmentation and between both eyes vs single-eye models. Attention heatmaps obtained from patients with AD highlighted regions surrounding small vascular branches as areas of highest relevance to the model decision making. A bidirectional encoder representations from transformers style self-supervised convolutional neural network pretrained on a large data set of retinal color photographs alone can screen symptomatic AD with high accuracy, better than U-Net-pretrained models. To be translated in clinical practice, this methodology requires further validation in larger and diverse populations and integrated techniques to harmonize fundus photographs and attenuate the imaging-associated noise.
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APA 7th
Dumitrascu, O. M., Li, X., Zhu, W., Woodruff, B. K., Nikolova, S., Sobczak, J., et al. (2024). Color Fundus Photography and Deep Learning Applications in Alzheimer Disease. Mayo Clinic proceedings. Digital health. https://doi.org/10.1016/j.mcpdig.2024.08.005
Vancouver
Dumitrascu OM, Li X, Zhu W, Woodruff BK, Nikolova S, Sobczak J, et al. Color Fundus Photography and Deep Learning Applications in Alzheimer Disease. Mayo Clinic proceedings. Digital health. 2024. doi:10.1016/j.mcpdig.2024.08.005. PMID: 39748801.
BibTeX
@article{dumitrascu2024-39748801,
author = {Dumitrascu, Oana M and Li, Xin and Zhu, Wenhui and Woodruff, Bryan K and Nikolova, Simona and Sobczak, Jacob and Youssef, Amal and Saxena, Siddhant and others},
title = {{Color Fundus Photography and Deep Learning Applications in Alzheimer Disease}},
journal = {Mayo Clinic proceedings. Digital health},
year = {2024},
doi = {10.1016/j.mcpdig.2024.08.005},
pmid = {39748801},
url = {https://doi.org/10.1016/j.mcpdig.2024.08.005}
}
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.
Key findings
- The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P<.001) and our institutional testing data sets (98.90% vs 94.17%; P=.04).
- No major differences were noted between the original and binary vessel segmentation and between both eyes vs single-eye models.
- Attention heatmaps obtained from patients with AD highlighted regions surrounding small vascular branches as areas of highest relevance to the model decision making.
- A bidirectional encoder representations from transformers style self-supervised convolutional neural network pretrained on a large data set of retinal color photographs alone can screen symptomatic AD with high accuracy, better than U-Net-pretrained models.
- To be translated in clinical practice, this methodology requires further validation in larger and diverse populations and integrated techniques to harmonize fundus photographs and attenuate the imaging-associated noise.
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Study details
- Effect size98.27%; 77.20%; 98.90%
- Evidence strengthLow
How this study scored 46/100
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 journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 26 of the 56 points we could judge it on, which is 46 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Mayo Clinic proceedings. Digital health) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 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 | 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39748801) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 26 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 26 of the 56 points we could judge it on is 46 out of 100. |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
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