Automated vision screening of children using a mobile graphic device
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Can measuring interocular brightness disparity, acuity, and colour vision classify children with amblyopia? SUBJECTS/METHODS: Two hundred eight subjects (3-14 years) were recruited for a prospective, observational protocol to measure interocular brightness disparity, uniocular acuities with and without a pinhole, and colour vision using an iPad. Subjects looked through polarizing filters and chose the brighter of two spaceships to measure interocular brightness disparity. The differential brightness of image pairs was varied through a staircase algorithm until equal brightness was perceived. Acuities and colour vision were tested with tumbling Es and AO-HRR colour plates, respectively. Unilateral amblyopia was later confirmed in two subjects. RESULTS: Binocular brightness balance on the iPad detected amblyopes with 100% sensitivity and specificity. Using 20/30 as cutoff for normal acuity, 1 of the amblyopes was detected, and non-amblyopes were excluded by visual acuity pinhole testing. The mean difference between iPad and E-Chart visual acuities with pinhole was 0.02 logMAR with limits of agreement from -0.08 to +0.11 logMAR. iPad and printed plates Colour vision testing produced identical results. Testing times were brief and exit pleasure responses were positive. Mean and range testing times for Brightness Sense, Colour vision, and Visual Acuity were 32.7 s (range = 12-63 s), 52.8 min (range = 17-95 s), and 88.75 s (range = 41-188 s), respectively. CONCLUSIONS: Interocular brightness disparity, acuity, and colour vision can be measured in children as young as 3 years old solely through playing a game on a mobile device. Interocular brightness disparity is a sensitive and specific method to detect unilateral amblyopia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".