Three-dimensional spatiotemporal analysis for the assessment of retinal capillary perfusion using a clinical OCT system
Bibliographic record
Abstract
Growing evidence suggests that subtle changes in retinal microcirculation may precede structural damage in vision-threatening diseases. Among these, perfusion heterogeneity within the retinal capillary network has emerged as a promising biomarker for early detection and disease monitoring. Recent advances in optical coherence tomography (OCT) and OCT-based angiography (OCTA) have enabled high-resolution, three-dimensional imaging of retinal morphology and vasculature. However, commercial systems remain limited in their ability to accurately analyze retinal perfusion dynamics due to reliance on proprietary and undisclosed post-processing algorithms. This paper introduces an effective protocol for spatial and temporal analysis of capillary perfusion heterogeneity using unprocessed OCTA volume data acquired by a commercial retinal imaging system. The proposed method employs a novel analysis utilizing the depth-resolved pixel-wise coefficient of variation (CoV) to quantitatively estimate retinal capillary perfusion heterogeneity. Comparison between the proposed method and conventional CoV analysis emphasizes the reliability of the new approach, incorporating depth-dependent signals. By using unprocessed OCTA data, the proposed method can provide more accurate measurements of retinal perfusion heterogeneity. Furthermore, the image processing techniques developed in this study could serve as a foundation for future research in other retinal vascular disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".