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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".