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Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging
C Ellis Wisely, Dong Wang, Ricardo Henao, Dilraj S Grewal, Atalie C Thompson, Cason B Robbins, Stephen P Yoon, Srinath Soundararajan et al.. The British journal of ophthalmology, 2020-11-26
Topic: Brain imaging, connectivity and assessment
284 eyes of 159 subjects (222 eyes from 123 cognitively healthy subjects and 62 eyes from 36 subjects with AD) were used to develop the model. Area under the receiving operating characteristic curve (AUC) values for predicted probability of AD for the independent test set varied by input used: UWF colour AUC 0.450 (95% CI 0.282, 0.592), OCTA SCP 0.582 (95% CI 0.440, 0.724), UWF FAF 0.618 (95% CI 0.462, 0.773), GC-IPL maps 0.809 (95% CI 0.700, 0.919). A model incorporating all images, quantitative data and patient data (AUC 0.836 (CI 0.729, 0.943)) performed similarly to models only incorporating all images (AUC 0.829 (95% CI 0.719, 0.939)). GC-IPL maps, quantitative data and patient data AUC 0.841 (95% CI 0.739, 0.943). Our CNN used multimodal retinal images to successfully predict diagnosis of symptomatic AD in an independent test set. GC-IPL maps were the most useful single inputs for prediction. Models including only images performed similarly to models also including quantitative data and patient data.
Key findings
- 284 eyes of 159 subjects (222 eyes from 123 cognitively healthy subjects and 62 eyes from 36 subjects with AD) were used to develop the model.
- Area under the receiving operating characteristic curve (AUC) values for predicted probability of AD for the independent test set varied by input used: UWF colour AUC 0.450 (95% CI 0.282, 0.592), OCTA SCP 0.582 (95% CI 0.440, 0.724), UWF FAF 0.618 (95% CI 0.462, 0.773), GC-IPL maps 0.809 (95% CI 0.700, 0.919).
- A model incorporating all images, quantitative data and patient data (AUC 0.836 (CI 0.729, 0.943)) performed similarly to models only incorporating all images (AUC 0.829 (95% CI 0.719, 0.939)).
- GC-IPL maps, quantitative data and patient data AUC 0.841 (95% CI 0.739, 0.943).
- Our CNN used multimodal retinal images to successfully predict diagnosis of symptomatic AD in an independent test set.
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Study details
- Participants159
- Effect sizeAUC 0.450; AUC 0.836; AUC 0.829
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design 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 31 of the 56 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (The British journal of ophthalmology) |
| 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 | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 159 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| 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 33243829) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 31 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 31 of the 56 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
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APA 7th
Wisely, C. E., Wang, D., Henao, R., Grewal, D. S., Thompson, A. C., Robbins, C. B., et al. (2022). Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging. The British journal of ophthalmology. https://doi.org/10.1136/bjophthalmol-2020-317659
Vancouver
Wisely CE, Wang D, Henao R, Grewal DS, Thompson AC, Robbins CB, et al. Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging. The British journal of ophthalmology. 2022. doi:10.1136/bjophthalmol-2020-317659. PMID: 33243829.
BibTeX
@article{wisely2022-33243829,
author = {Wisely, C Ellis and Wang, Dong and Henao, Ricardo and Grewal, Dilraj S and Thompson, Atalie C and Robbins, Cason B and Yoon, Stephen P and Soundararajan, Srinath and others},
title = {{Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging}},
journal = {The British journal of ophthalmology},
year = {2022},
doi = {10.1136/bjophthalmol-2020-317659},
pmid = {33243829},
url = {https://doi.org/10.1136/bjophthalmol-2020-317659}
}