AR stereoscopic 3D Human Eye Examination App
Bibliographic record
Abstract
The fundus eye exam, an important ophthalmologic assessment procedure that allows examining the eye's health, is taught by demonstration and guided practices whereby the trainees practice on each other and expertise is gained through experience using an ophthalmoscope. However, in addition to the issues associated with such an apprenticeship model, the anatomy of the eye's intricate oculomotor system is conceptually difficult for novice trainees to grasp. The examination is based on 2D eye fundus images that without proper training and skills abnormalities in the eye can be overlooked. Although virtual anatomy and simulators are available to alleviate some of these issues, these still require an elevated investment and infrastructure and are typically limited to one user at a time. Our ongoing work is seeing the development of an engaging and interactive stereoscopic augmented reality app. The app allows a student to navigate, in an immersive stereoscopic 3D environment, the inner volumetric shape of the eye important to detect features and pathologies.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.140 | 0.042 |
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".