Validation of the visual acuity iPad app Eye Chart Pro compared to the standard Early Treatment Diabetic Retinopathy Study chart in a low-vision population
Bibliographic record
Abstract
Introduction A low-vision assessment (LVA) is central to developing a vision rehabilitation plan. However, access to LVAs is often limited by the quantity and geographic distribution of low-vision providers, as well as patient-centred transportation challenges. A tablet-based LVA tool kit, delivered virtually, has the potential to overcome many of these barriers. The purpose of this research was to validate a key component of the tablet-based tool kit – a commercially available iPad visual acuity (VA) test (Eye Chart Pro) iPad app – in a low-vision population. Methods Participants with low vision ( n = 26) and those who were normally sighted ( n = 25) underwent VA testing with both the iPad VA test application and the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The VA data were compared using a t-test, linear regression and Bland–Altman analysis. Results There was no significant difference in the mean absolute difference in VA (log of minimum angle of resolution (logMAR)=0.11; p = 0.82). Eye Chart Pro and Standard ETDRS Chart measures were also not significantly different ( p = 0.98). However, there were significant differences between test methods in the low-vision group and the normally sighted group ( p > 0.0001 and p = 0.007, respectively). The Bland–Altman analysis showed a mean bias (difference) of –0.0005 logMAR between methods, and 95% limits of agreement of 0.298 and –0.299 logMAR. Discussion The ETDRS chart function on the Eye Chart Pro application can reliably measure VA across a range, from normally sighted patients to those with low vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".