Home › Research › Cognitive tests and assessment
Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment
Haque RU, Pongos AL, Manzanares CM, Lah JJ, Levey AI, Clifford GD.. IEEE transactions on bio-medical engineering, 2020-12-21
Topic: Brain imaging, connectivity and assessment
Objective Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task. Methods We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals. Results Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking. Conclusion This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment. Significance Given the ubiquity of tablet devices, our approach has the potential to scale globally.
Key findings
- Results Mild to severe cognitive impairment was identifiable with a test accuracy of 70%.
Study details
- Participants250
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 and multi-centre collaboration, so those were left out: it earned 28 of the 58 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on bio-medical engineering) |
| 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 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 250 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 32340935) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 28 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 28 of the 58 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
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. Check the citation against the original before you use it.
APA 7th
Haque, R. U., Pongos, A. L., Manzanares, C. M., Lah, J. J., Levey, A. I., & Clifford, G. D. (2021). Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment. IEEE transactions on bio-medical engineering. https://doi.org/10.1109/tbme.2020.2990734
Vancouver
Haque RU, Pongos AL, Manzanares CM, Lah JJ, Levey AI, Clifford GD. Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment. IEEE transactions on bio-medical engineering. 2021. doi:10.1109/tbme.2020.2990734. PMID: 32340935.
BibTeX
@article{haque2021-32340935,
author = {Haque, R. U. and Pongos, A. L. and Manzanares, C. M. and Lah, J. J. and Levey, A. I. and Clifford, G. D.},
title = {{Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment}},
journal = {IEEE transactions on bio-medical engineering},
year = {2021},
doi = {10.1109/tbme.2020.2990734},
pmid = {32340935},
url = {https://doi.org/10.1109/tbme.2020.2990734}
}
Related studies in Cognitive tests and assessment
Also tagged “convolutional”
205 studies in the library carry the topic tag “convolutional”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 205 tagged studies.