Computer-based Real-Time Analysis in Mobile Ocular Screening
Bibliographic record
Abstract
Mobile ocular telemedicine is potentially an effective method to provide service in medically underserved areas and to screen large populations for abnormalities. Currently, digital images are acquired, stored, and transferred to readers for evaluation, after which the results are provided to the subjects. The transfer of large image files and the timeliness of the subsequent reading of images are significant factors for practical implementation of effective telemedicine screening. This work examines the feasibility of in situ real-time computer analysis of digital images to determine and classify the image results as normal and abnormal. This retrospective study used a photoscreening database of 360 patients ranging in ages from 6 months to 18 years. Computer analysis automatically classified the binocular photorefraction (PR) images, and these PR results were compared to those of the subjective clinical eye examinations provided. With an average processing time of approximately 15 seconds per examinee, the analysis found that the PR results can be categorized as: a positive group that requires referral (186 cases) with a predictive value of 98.9% (2 false-positives); a negative group (144 cases) with a predictive value of 89.6% (15 false-negatives); and an uncertain group (30 cases or 8.3%) that required resolution by readers. The real-time analysis code reduces by approximately 92% the manpower for image grading and electronic transmission at this stage of ocular evaluation. These results indicate the feasibility of this approach.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".