Automated visual acuity estimation by optokinetic nystagmus using a stepped sweep stimulus
Bibliographic record
Abstract
Abstract Purpose To describe an automatic system for objective measurement of visual acuity (VA) using optokinetic nystagmus (OKN). This pilot study tested the system's sensitivity and specificity for detecting reduced VA in healthy adults by comparing VA‐OKN to VA with an Early Treatment of Diabetic Retinopathy Study (ETDRS) chart (VA‐ETDRS). Methods Adult participants (age 30 ± 12 years) with either reduced VA ( n = 11, VA‐ETDRS > 0.20 logMAR) or normal VA ( n = 12, VA‐ETDRS ≤ 0.20 logMAR) completed monocular VA‐OKN measurements in each eye. The VA‐OKN stimulus was an array of drifting (5°/s) vanishing discs presented in descending/ascending size order (0.00–1.00 logMAR in 0.10 steps). The stimulus was stepped every 2 s, and 10 sweeps were shown per eye (five ascending and five descending). Eye‐tracking data determined when OKN activity ceased (descending sweep) or began (ascending sweep), which was used to determine VA‐OKN for each sweep. The estimates were averaged across sweeps to produce an automated VA‐OKN. The automated sweeps were then provided in randomised order to a reviewer blinded to the VA‐ETDRS findings who determined a final VA‐OKN for an eye. Results A single randomly selected eye from each observer was used for analysis. The sensitivity and specificity of VA‐OKN using the same 0.20 logMAR threshold as VA‐ETDRS was 100%. Comparisons between the VA‐OKN and VA‐ETDRS measures were made for participants in the reduced VA group. There was no significant difference between VA‐OKN and VA‐ETDRS ( p = 0.55) and the two measures produced comparable values ( r 2 = 0.84, 95% limits of agreement = 0.19 logMAR, intra‐class correlation coefficient = 0.90 [95% CI:0.68–0.97]). Conclusions Visual acuity using optokinetic nystagmus correctly identified a VA deficit in adults and for those with a VA deficit, VA‐OKN was strongly correlated with the gold‐standard clinical measure of VA. OKN is a promising method which has the potential for use 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".