Evaluation of a smartphone photoscreening app to detect refractive amblyopia risk factors in children aged 1–6 years
Bibliographic record
Abstract
PURPOSE: To determine the specificity and sensitivity of a smartphone app (GoCheckKids [GCK] used as a photoscreening tool on the iPhone 7 to detect refractive amblyopia risk factors in children aged 1-6 years. PARTICIPANTS AND METHODS: A prospective, multicenter, 10-month evaluation of children aged 1-6 years old who underwent photoscreening with the GCK app to detect amblyopia risk factors. The first acceptable quality photograph of each study subject was evaluated by trained technicians using GCK's proprietary automated image processing algorithm to analyze for amblyopia risk factors. Trained graders, masked to the cycloplegic clinical data, remotely reviewed photographs taken with the app and compared results to the gold standard pediatric ophthalmology examinations using the 2013 American Association for Pediatric Ophthalmology & Strabismus amblyopia risk factor guidelines. Primary outcome was the ability of the GCK app to identify amblyopia risk factors compared to the cycloplegic refraction. RESULTS: There were 287 patient images analyzed. The overall sensitivity and specificity in detecting amblyopia risk factors were 76% and 85%, respectively using manual grading. The overall automated grading results had a sensitivity and sensitivity in detecting amblyopia risk factors of 65% and 83%, respectively. CONCLUSION: The GCK smartphone app is a viable photoscreening device for the detection of amblyopia risk factors in children aged 1-6 years.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".