In-vivo imaging of keratoconic corneas using high-speed high-resolution swept-source OCT
Bibliographic record
Abstract
Keratoconus (KC) is a progressive degenerative corneal disease that can lead to a strong deformation of the cornea and loss of clarity, causing distorted or blurred vision. Surgical treatment for severe cases requires precise evaluation of the corneal curvature, thickness, layer structure, and clarity. Current clinical instruments for assessing the corneal shape cannot resolve the internal structure, and high-resolution microscopy techniques are limited to a small field of view. We have implemented a swept-source OCT (SS-OCT) system that enables high-speed imaging (100 kA-scans/s) of the entire cornea and provides ~5.1μm axial resolution in corneal tissue. With an imaging range of 5.6 mm (in air), we can cover the full length from the cornea’s apex to the anterior surface of the lens. We have acquired volumetric corneal images from human subjects with different stages of KC and from subjects who underwent surgery or cross-linking therapy. We developed an automatic algorithm for segmenting the outer and inner surfaces of the cornea in the images which will enable precise measurement of the corneal curvature and thickness. This makes SS-OCT an ideal instrument for comprehensive examination of keratoconic corneas.
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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 | 0.000 |
| 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".