Application of VR eye movement cognitive assessment in the early screening of cognitive impairment
Bibliographic record
Abstract
Abstract Background: Dementia is a rapid growing global health challenge, and early screening in the preclinical stage is necessary. Mild cognitive impairment (MCI) is considered a transitional stage preceding dementia, and current diagnostic markers for AD are limited by cost and invasiveness. Neuropsychological tests (such as MMSE, MoCA) are valid but neither simple nor efficient enough to serve as large-scale dementia screening tools. Eye-tracking data can be encoded as cognitive activity and states, which provides quantitative and multi-dimensional attributes of cognitive function. Its application to cognitive assessment has shown promise in identifying patients with MCI or dementia. Immersive environments of virtual reality technology guarantee the integrity of eye-tracking data and the portability of VR headset enables efficient large-scale early screening of cognitive impairment in communities. Objective: Develop a 5-minute dementia screening tool — VR Eye Movement Cognitive Assessment to help physicians detect cognitive impairment as an alternative approach of traditional paper-based instruments. Methods: 201 subjects from Shenzhen Baoan Chronic Hospital were administered MoCA and VR eye movement cognitive assessments. Raw gaze data was captured by eye tracker of the VR headset and filtered as eye movements which would be encoded as features. Machine learning models were established as the predictor of MoCA score and the classifier of cognitive impairment of three education-based groups within which optimal cut-off score was given. Results: Support vector regression was proposed as the VR-AI model and achieved high correlation of 0.9 with MoCA score, greater than baseline model of 0.58. Optimal cut-off scores (less than 6 years of education: 14/15; 6 to 9 years of education: 18/19; more than 9 years of education: 23/24) can well distinguish normal and cognitively impaired subjects — with overall sensitivity of 88.5% and specificity of 83%. Conclusion: VR eye movement cognitive assessment is a portable, efficient, and quantitative dementia screening tool, which can be used for early screening of mild cognitive impairment and dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".