Wide field imaging biomarkers: A different perspective
Bibliographic record
Abstract
Wide field retinal imaging has emerged as a transformative technology over the last few decades, revolutionizing our ability to visualize the intricate landscape of the retina. By capturing expansive retinal areas, these techniques offer a panoramic view going beyond traditional imaging methods. In this review, we explore the significance of retinal imaging-based biomarkers to help diagnose ocular and systemic conditions. We discuss quantitative biomarkers, including ischemic index, nonperfusion area and more, and their application in diabetic retinopathy, central retinal vein occlusion, neurodegenerative diseases, and more. In addition, we outline qualitative biomarkers such as choroidal venous hyperpermeability and intervortex anastomoses. The role of wide field fundus autofluorescence in assessing hereditary retinal diseases is also emphasized. Standardized imaging procedures, professional collaboration, and validation across a range of clinical circumstances are necessary for the effective use of these biomarkers. They have the potential to transform disease identification, risk assessment, and customize therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".