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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".