Comparative study between ViewMind Atlas™, a novel digital measure of cognition, and traditional neuropsychological tests
Bibliographic record
Abstract
Background: Cognitive impairment (CI) presents significant challenges in neurodegenerative conditions and aging populations. Traditional cognitive assessment tools often face limitations such as high costs, accessibility barriers, and lengthy administration times. ViewMind Atlas™, a digital biomarker combining eye-tracking technology and artificial intelligence, provides a novel, efficient approach to cognitive evaluation. This study aimed to evaluate the capacity of ViewMind Atlas™ to detect CI, correlate its results with the Montreal Cognitive Assessment (MoCA) and other neuropsychological tests, and assess its clinical validity. Method: This cross-sectional, single-centre study included 154 participants aged 45–95 years with and without cognitive complaints. Cognitive assessments were performed using ViewMind Atlas™, which incorporates a head-mounted display (HMD) with integrated eye-tracking sensors. The system measured saccade amplitude, fixation duration, refixation rates, and response times during five validated visual tasks (Moving Dot, Go/No-Go, New Colors, Color Combinations, and n-back). Participants also completed the MoCA and a neuropsychological battery, including the Word Accentuation Test-Buenos Aires (WAT-BA), BEM-144 Logical Memory and Serial Learning, Rey Complex Figure Test, Digit Span and Matrix Reasoning (WAIS-III), Trail Making Tests A and B, Boston Naming Test, Phonological and Semantic Verbal Fluency Tests, and Clock Drawing Test. Statistical analyses included sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves, as well as correlations between ViewMind metrics and cognitive test scores. Result: ViewMind Atlas™ demonstrated a sensitivity of 78%, specificity of 70%, and balanced accuracy of 74% for CI detection. Significant correlations were observed between ViewMind metrics and MoCA scores and other neuropsychological test results (p <0.05), validating its ability to identify CI. Conclusion: ViewMind Atlas™, leveraging HMD-based eye-tracking technology, is a valid, efficient tool for detecting cognitive impairment. Its strong alignment with traditional cognitive assessments supports its potential for clinical and research applications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".