Variability of scan quality and perfusion density in longitudinal optical coherence tomography angiography imaging
Bibliographic record
Abstract
BACKGROUND/AIMS: Optical coherence tomography angiography (OCT-A) images are subject to variability, but the extent to which learning impacts OCT-A measurements is unknown. We determined whether there is a learning effect in glaucoma patients and healthy controls imaged with OCT-A. METHODS: Ninety-one open-angle glaucoma patients and 54 healthy controls were imaged every 4 months over a period of approximately 1 year in this longitudinal cohort study. We analysed 15°×15° scans, centred on the fovea, in one eye of each participant. Two-dimensional projection images for the superficial, intermediate and deep vascular plexuses were exported and binarised after which perfusion density was calculated. Linear mixed-effects models were used to investigate the association between perfusion density and follow-up time. RESULTS: The mean (SD) age of glaucoma patients and healthy controls was 67.3 (8.1) years and 62.1 (9.0) years, respectively. There was a significant correlation between perfusion density and scan quality in both glaucoma patients (r=0.50 (95% CI 0.42 to 0.58); p<0.05) and healthy controls (r=0.41 (95% CI 0.29 to 0.52); p<0.05). An increase in perfusion density occurred over time and persisted, even after adjustment for scan quality (1.75% per year (95% CI 1.14 to 2.37), p<0.01). CONCLUSIONS: Perfusion density measurements are subject to increasing experience of either the operator or participant, or a combination of both. These findings have implications for the interpretation of longitudinal measurements with OCT-A.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".