In-vivo volumetric imaging of the cellular structure of healthy and pathological human cornea with high-speed UHR-OCT (Conference Presentation)
Bibliographic record
Abstract
Degenerative conditions such as keratoconus and Fuch’s dystrophy can alter over time the cellular structure of the human corneal epithelium and endothelium respectively. A high-speed UHR-OCT system, capable of generating volumetric images of the cellular structure of the human cornea was built. The UHR-OCT system has a compact fiber-optic design that utilizes a commercial femtolaser with the central wavelength of 790 nm and 3dB spectral bandwidth of 150 nm to achieve ~ 1.4 µm axial resolution in corneal tissue. The optical design of the OCT imaging probe ensured ~2 µm OCT lateral resolution in corneal tissue. At the detection end of the UHR-OCT system, a high-resolution spectrometer (Cobra, Wasatch Photonics) is interfaced with a novel line scan camera. The camera has 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 a 10 dB roll-off over 1.5 mm scanning range for ~800 µm power of the imaging beam incident on the corneal surface. Volumetric images of healthy and pathological corneas were acquired in-vivo from healthy volunteers and subjects with keratoconus and Fuch’s dystrophy and the images were compared with typical histological images. 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 | 0.000 | 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) | 0.002 | 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".