Asymmetry analysis of macular optical coherence tomography angiography in patients with glaucoma and healthy subjects
Bibliographic record
Abstract
BACKGROUND/AIMS: Quantitative analysis of optical coherence tomography angiography (OCT-A) images requires a reproducible approach that accounts for sectoral loss. The objective of this study was to determine whether an index that accounts for both global (perfusion density, PD) and asymmetric loss of perfusion, rather than PD alone, more reliably measures loss of perfusion in patients with glaucoma. METHODS: We analysed macular OCT-A scans of 95 glaucoma patients and 59 control subjects. Two-dimensional projection images corresponding to the superficial vascular plexus were exported and analyses performed to calculate global PD and image asymmetry. An unsigned perfusion asymmetry index (PAI) that included PD and asymmetry (with 1:1 wt) was calculated. Perfusion density and PAI were compared with 10-2 visual field mean deviation and ganglion cell layer (GCL) thickness. RESULTS: Median (IQR) visual field mean deviation was -1.73 (-3.76, 0.30) dB for the glaucoma group and 0.67 (0.16, 1.18) dB for the control group. The strength of the correlation with mean deviation was stronger for PAI (r=0.47), compared with PD (r=0.35), whereas with GCL thickness they were comparable (r=0.45 and 0.43, respectively). Compared with controls, mean PD was 12% lower in patients with glaucoma (0.27 vs 0.30), while PAI was 17% lower (0.40 vs 0.48). However, diagnostic accuracy of either PD or PAI was worse than GCL thickness. CONCLUSIONS: While PAI yielded better correlation with mean deviation and GCL thickness, and a slightly improved separation between patients with glaucoma and healthy controls, diagnostic accuracy was inferior compared with GCL thickness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".