High speed UHR-OCT for in-vivo volumetric imaging of the palisades of Vogt and the cellular structure of the limbal crypts in the healthy and pathological human corneo-scleral limbus (Conference Presentation)
Bibliographic record
Abstract
Limbal stem cell dysfunction (LSCD) causes morphological and physiological changes in the limbus that result in decreased vision, photophobia, tearing, chronic inflammation and hyperemia, recurrent episodes of pain, and blindness in severe cases. Currently, clinical in-vivo imaging of the palisaded of Vogt (POV) and the cellular structure of the limbal crypts in the human corneo-scleral limbus is accomplished by in-vivo confocal microscopy (IVCM). However, IVCM requires physical contact with the limbal tissue that can cause pain and inflammation. In this study, we used a novel high speed, ultra-high resolution optical coherence tomography (UHR-OCT) system to generate volumetric, cellular resolution image of the healthy and pathological human corneo-scleral limbus. The UHR-OCT system has a compact fiber-optic design. A femtosecond laser with 790 nm central wavelength and ~150 nm spectral bandwidth (at 3dB) was used to achieve ~1.4 µm axial resolution in biological tissue. The UHR-OCT system also utilizes a high resolution spectrometer (Cobra, Wasatch Photonics) connected to a novel line scan camera with a tall pixel design, 2048 pixel array and a maximum readout rate of 250 kHz. The system’s SNR was 96 dB at 100 µm away from the zero delay line, with ~10 dB roll-off over 1.5 mm scanning range for ~800 µm power of the imaging beam. Volumetric images of the POV and the cellular structure of the limbal crypts were acquired in-vivo and without contact with the limbal tissue from healthy and LSCD and subjects. This study was approved by the University of Waterloo Research Ethics Committee.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".