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Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database
Denis Corbin, Frédéric Lesage. Scientific reports, 2022-04-06
Topic: Brain imaging, connectivity and assessment
Plain-language summary
Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease. Knowing from previous studies that these two factors can manifest in the retina, the aim was to investigate whether a deep learning method was able to predict the cognition of an individual from a RGB image of his retina and metadata. A deep learning model, EfficientNet, was used to predict cognitive scores from the Canadian Longitudinal Study on Aging (CLSA) database. The proposed model explained 22.4% of the variance in cognitive scores on the test dataset using fundus images and metadata. Metadata alone proved to be more effective in explaining the variance in the sample (20.4%) versus fundus images (9.3%) alone. Attention maps highlighted the optic nerve head as the most influential feature in predicting cognitive scores. The results demonstrate that RGB fundus images are limited in predicting cognition.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Corbin, D., & Lesage, F. (2022). Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database. Scientific reports. https://doi.org/10.1038/s41598-022-09719-3
Vancouver
Corbin D, Lesage F. Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database. Scientific reports. 2022. doi:10.1038/s41598-022-09719-3. PMID: 35388080.
BibTeX
@article{corbin2022-35388080,
author = {Corbin, Denis and Lesage, Frédéric},
title = {{Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database}},
journal = {Scientific reports},
year = {2022},
doi = {10.1038/s41598-022-09719-3},
pmid = {35388080},
url = {https://doi.org/10.1038/s41598-022-09719-3}
}
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Key findings
- Metadata alone proved to be more effective in explaining the variance in the sample (20.4%) versus fundus images (9.3%) alone.
- The results demonstrate that RGB fundus images are limited in predicting cognition.
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Study details
- Study designlongitudinal study
- Effect size22.4%; 20.4%; 9.3%
- Evidence strengthModerate
How this study scored 61/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people and randomized / 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, multicenter collaboration and free to read, so those were left out: it earned 34 of the 56 points we could judge it on, which is 61 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 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) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35388080) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 34 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 34 of the 56 points we could judge it on is 61 out of 100. |
| Total | 61 / 100 | — |
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