Automated visual acuity estimation by optokinetic nystagmus using a stepped sweep stimulus
Bibliographic record
Abstract
ABSTRACT Purpose Measuring visual acuity (VA) can be challenging in adults with cognitive impairment and young children. We developed an automatic system for measuring VA using Optokinetic Nystagmus (OKN). Methods VA-OKN and VA by ETDRS (VA-ETDRS) were measured monocularly in healthy participants (n=23, age 30±12). VA was classified as reduced (n=22, >0.2 logMAR) or not (n=24, ≤0.2 logMAR) in each eye. VA-OKN stimulus was an array of drifting (5 deg/sec) vanishing disks presented in descending/ascending size order (0.0 to 1.0 logMAR in 0.1 logMAR steps). The stimulus was stepped every 2 seconds, and 10 sweeps were shown per eye. Eye tracking data determined when OKN activity ceased (descending sweep) or began (ascending sweep) to give an automated sweep VA. Sweep traces were randomized and assessed by a reviewer blinded to VA-ETDRS. A final per sweep VA and VA-OKN was thereby determined. Results A single randomly selected eye was used for analysis. VA deficit group: There was no significant difference between overall mean VA-OKN and VA-ETDRS (p>0.05, paired t-test) and the r 2 statistic was 0.84. The 95% limits of agreement were 0.19 logMAR. No VA deficit group: There was a 0.24 logMAR bias between VA-OKN and VA-ETDRS and no correlation was found (r 2 = 0.06). However, the overall sensitivity/specificity for classification was 100%. Conclusions A robust correlation between VA-ETDRS and VA-OKN was found. The method correctly detected a VA deficit. Translational relevance OKN is a promising method for measuring VA in cognitively impaired adults and pre-verbal children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".