Advanced imaging of corneal neovascularisation with a novel swept-source AS-OCTA system
Bibliographic record
Abstract
Aims To explore the clinical application of a novel swept-source anterior segment optical coherence tomography angiography (AS-OCTA) system for imaging corneal neovascularisation (CoNV), assessing limbal vasculature and detecting short-term vascular changes following pharmacologic vasoconstriction. Methods This cross-sectional observational study included 20 eyes: 10 with clinically diagnosed CoNV of various aetiologies and 10 healthy controls. Each eye underwent AS-OCTA imaging using the BMizar 400kHz Full-Range Swept-Source OCT system at baseline and after administration of 10% phenylephrine (PE). FlowArea (mm 2 ) was automatically calculated across predefined circular regions of interest (ROIs) encompassing the cornea, limbus and episclera. In corneal and limbal ROIs, en face images were segmented into three depth layers: total (epithelium to endothelium), superficial (epithelium to 150 µm) and deep (150 µm to endothelium). Results CoNV eyes exhibited significantly higher total and deep corneal FlowArea compared with controls (p=0.0002 and p=0.0005, respectively). Post PE, a significant reduction in FlowArea was observed in the total and deep corneal layers of CoNV eyes (p=0.006 and p=0.009, respectively), and in the limbal region of both groups, with significant reductions observed in CoNV and controls (p=0.006 and p=0.003, respectively). Conclusions This AS-OCTA platform enabled high-resolution, depth-resolved visualization of CoNV and revealed vascular changes following topical vasoconstriction. The extended field of view allowed simultaneous assessment of corneal, limbal and episcleral vasculature within a single scan session, supporting its potential role in both diagnostic evaluation and longitudinal monitoring of anterior segment diseases.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".