The Effect of Compression on Clinical Diagnosis of Glaucoma Based on Non-analyzed Confocal Scanning Laser Ophthalmoscopy Images
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To evaluate the effect of different image compression formats of non-analyzed Heidelberg Retina Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) images on the diagnosis of glaucoma by ophthalmologists. MATERIALS AND METHODS: Thirty-three topographic and reflectance images taken with the HRT representing different levels of disease were transformed using nine different compression formats. Three independent ophthalmologists, masked as to contour line and stereometric parameters, classified the original and compressed HRT images as normal, suspected glaucoma, or glaucoma, and Kappa agreement coefficients were calculated. RESULTS: The Tagged Image File Format had the largest file size and the Joint Photographic Experts Group (JPEG) 2000 format had the smallest size. The highest Kappa coefficient value was 1.00 for all ophthalmologists using the Tagged Image File Format. Kappa values for JPEG formats were all in the range of good to excellent agreement. Kappa values were lower for Portable Network Graphic and Graphics Interchange Format compression formats. CONCLUSION: Image compression with JPEG 2000 at a ratio of 20:1 provided sufficient quality for glaucoma analysis in conjunction with a relatively small image size format, and may prove to be attractive for HRT telemedicine applications. Further clinical studies validating the usefulness of interpreting non-analyzed HRT images are required.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".