In Vivo Contactless, Cellular-Resolution Imaging of the Healthy and Pathological Human Limbus With 250-kHz Point-Scanning SD-OCT
Bibliographic record
Abstract
Purpose: To demonstrate that high-seed, ultra-high-resolution spectral-domain optical coherence tomography (SD-OCT) technology can image in vivo fine morphological features in the healthy and pathological human limbus. Methods: A compact, fiberoptic SD-OCT system was developed for imaging the human limbus. It combines ∼1.5-µm isotropic spatial resolution in ocular tissue and an acquisition rate of 250,000 A-scans per second. The imaging probe was outfitted with two microscope objectives to provide flexibility in the choice of wide field of view and extended depth of focus versus high lateral resolution. The clinical potential of the system was evaluated by imaging subjects with limbal stem cell dysfunction (LSCD; n = 4) and healthy controls (n = 6). Results: Limbus images acquired from the healthy controls showed normal cellular structure of the limbal crypts, palisades of Vogt (POVs), and vasculature of the underlying scleral tissue. Images acquired from the LSCD subjects showed distortions or absence of POVs, invasion of highly scattering conjunctival tissue over the limbal and peripheral corneal epithelium, scarring and thinning of the limbal epithelium, and neovascularization. Conclusions: The combination of high OCT spatial resolution and rapid image acquisition rate allows for in vivo, contactless, volumetric visualization of fine morphological details that could be beneficial for the precise diagnosis and grading of LSCD, planning of treatment, and evaluation of the effectiveness of the treatment approaches. Translational Relevance: The OCT technology described here could improve the clinical diagnostics and grading of LSCD, preoperative planning, and postoperative evaluation of LSCD subjects, in addition to monitoring the effectiveness of various LSCD treatments.
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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 | 0.000 |
| 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) | 0.001 | 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".