Label-Free Density Measurements of Radial Peripapillary Capillaries in the Human Retina
Bibliographic record
Abstract
Radial peripapillary capillaries (RPCs) comprise a unique network of capillary beds within the retinal nerve fibre layer (RNFL) and play a critical role in satisfying the nutritional requirements of retinal ganglion cell (RGC) axons. Understanding the topographical and morphological characteristics of these networks through in vivo techniques may improve our understanding about the role of RPCs in RGC axonal health and disease. This study utilizes a novel, non-invasive and label-free optical imaging technique, speckle variance optical coherence tomography (svOCT), for quantitatively studying RPC networks in the human retina. Six different retinal eccentricities from 16 healthy eyes were imaged using svOCT. The same eccentricities were histologically imaged in 9 healthy donor eyes with a confocal scanning laser microscope. Donor eyes were subject to perfusion-based labeling techniques prior to retinal dissection, flat mounting and visualization with the microscope. Capillary density and diameter measurements from each eccentricity in svOCT and histological images were compared. Data from svOCT images were also analysed to determine if there was a correlation between RNFL thickness and RPC density. The results are as follows: (1) The morphological characteristics of RPC networks on svOCT images are comparable to histological images; (2) With the exception of the nasal peripapillary region, there were no significant differences in RPC density measurements between svOCT and histological images; (3) Capillary diameter measurements were significantly greater in svOCT images compared to histology; (4) There is a positive correlation between RPC density and RNFL thickness. The findings in this study suggest that svOCT is a reliable modality for analyzing RPC networks in the human retina. It may therefore be a valuable tool for aiding our understanding about vasculogenic mechanisms that are involved in RGC axonopathies. Further work is required to explore the reason for some of the quantitative differences between svOCT and histology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".