Home › Research › Digital health tools
Classification and staging of Parkinson's disease using video-based eye tracking
Brien DC, Riek HC, Yep R, Huang J, Coe B, Areshenkoff C, Grimes D, Jog M et al.. Parkinsonism & related disorders, 2023-02-08
Topic: Amyloid, tau and blood-test diagnosis
Introduction 83% of those diagnosed with Parkinson's Disease (PD) eventually progress to PD with mild cognitive impairment (PD-MCI) followed by dementia (PDD) - suggesting a complex spectrum of pathology concomitant with aging. Biomarkers sensitive and specific to this spectrum are required if useful diagnostics are to be developed that may supplement current clinical testing procedures. We used video-based eye tracking and machine learning to develop a simple, non-invasive test sensitive to PD and the stages of cognitive dysfunction. Methods From 121 PD (45 Cognitively Normal/45 MCI/20 Dementia/11 Other) and 106 healthy controls, we collected video-based eye tracking data on an interleaved pro/anti-saccade task. Features of saccade, pupil, and blink behavior were used to train a classifier to predict confidence scores for PD/PD-MCI/PDD diagnosis. Results The Receiver Operator Characteristic Area Under the Curve (ROC-AUC) of the classifier was 0.88, with the cognitive-dysfunction subgroups showing progressively increased AUC, and the AUC of PDD being 0.95. The classifier reached a sensitivity of 83% and a specificity of 78%. The confidence scores predicted PD motor and cognitive performance scores. Conclusion Biomarkers of saccade, pupil, and blink were extracted from video-based eye tracking to create a classifier with high sensitivity to the landscape of PD cognitive and motor dysfunction. A complex landscape of PD is revealed through a quick, non-invasive eye tracking task and our model provides a framework for such a task to be used as a supplementary screening tool in the clinic.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Brien, D. C., Riek, H. C., Yep, R., Huang, J., Coe, B., Areshenkoff, C., et al. (2023). Classification and staging of Parkinson's disease using video-based eye tracking. Parkinsonism & related disorders. https://doi.org/10.1016/j.parkreldis.2023.105316
Vancouver
Brien DC, Riek HC, Yep R, Huang J, Coe B, Areshenkoff C, et al. Classification and staging of Parkinson's disease using video-based eye tracking. Parkinsonism & related disorders. 2023. doi:10.1016/j.parkreldis.2023.105316. PMID: 36822878.
BibTeX
@article{brien2023-36822878,
author = {Brien, D. C. and Riek, H. C. and Yep, R. and Huang, J. and Coe, B. and Areshenkoff, C. and Grimes, D. and Jog, M. and others},
title = {{Classification and staging of Parkinson's disease using video-based eye tracking}},
journal = {Parkinsonism \& related disorders},
year = {2023},
doi = {10.1016/j.parkreldis.2023.105316},
pmid = {36822878},
url = {https://doi.org/10.1016/j.parkreldis.2023.105316}
}
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
- Results The Receiver Operator Characteristic Area Under the Curve (ROC-AUC) of the classifier was 0.88, with the cognitive-dysfunction subgroups showing progressively increased AUC, and the AUC of PDD being 0.95.
- A complex landscape of PD is revealed through a quick, non-invasive eye tracking task and our model provides a framework for such a task to be used as a supplementary screening tool in the clinic.
Why it matters
A video-based eye tracking classifier distinguished Parkinson's disease and its cognitive stages from healthy controls with an ROC-AUC of 0.88, reaching 0.95 for Parkinson's disease dementia. This concerns Parkinson's disease, not Alzheimer's disease, and does not test any treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Evidence strengthLow
How this study scored 48/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Parkinsonism & related disorders) |
| Journal's citation record | 2 / 4 | Parkinsonism & Related Disorders: journal h-index 155 (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 | 3 / 5 | 2023 — 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 | 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 | 4 / 7 | Cited 75 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 15.0 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 11.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 3 / 4 | Cited 66 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Douglas P. Munoz: h-index 84 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Toronto: institution h-index 1,923 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 17 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36822878) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
1 study in the alzhub library lists this one in its references.
Related studies in Digital health tools
Also tagged “bodies”
1,017 studies in the library carry the topic tag “bodies”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 1,017 tagged studies.