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Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network
Alexander Richardson, Anita Kundu, Ricardo Henao, Terry Lee, Burton L Scott, Dilraj S Grewal, Sharon Fekrat. Translational vision science & technology, 2024-08-01
Topic: Plasma and spinal-fluid biomarkers
In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. TRANSLATIONAL RELEVANCE: Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.
Key findings
- In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing.
- Our best CNN variant achieved an AUC of 0.918.
- UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory.
- Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms.
- TRANSLATIONAL RELEVANCE: Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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 24 of the 56 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Translational vision science & technology) |
| 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 39136960) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 24 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 24 of the 56 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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 names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Richardson, A., Kundu, A., Henao, R., Lee, T., Scott, B. L., Grewal, D. S., et al. (2024). Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network. Translational vision science & technology. https://doi.org/10.1167/tvst.13.8.23
Vancouver
Richardson A, Kundu A, Henao R, Lee T, Scott BL, Grewal DS, et al. Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network. Translational vision science & technology. 2024. doi:10.1167/tvst.13.8.23. PMID: 39136960.
BibTeX
@article{richardson2024-39136960,
author = {Richardson, Alexander and Kundu, Anita and Henao, Ricardo and Lee, Terry and Scott, Burton L and Grewal, Dilraj S and Fekrat, Sharon},
title = {{Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network}},
journal = {Translational vision science \& technology},
year = {2024},
doi = {10.1167/tvst.13.8.23},
pmid = {39136960},
url = {https://doi.org/10.1167/tvst.13.8.23}
}