Powell lens-based Line-Field OCT for in-vivo, contact-less, cellular resolution imaging of the human cornea
Bibliographic record
Abstract
Potentially blinding corneal diseases alter the morphology of the human cornea. At the early stages of disease development, these changes occur at cellular level. The ability to visualize and quantify such changes can lead to early diagnostics, which is pivotal for the long-term preservation of vision. Here we present a novel Powell Line-based Line-Field Optical Coherence Tomography (PL-LF-OCT) system that combines high spatial resolution (2.4 μm × 2.2 μm × 1.7 μm (x × y × z)) in biological tissue, sufficient to resolve individual cells, high sensitivity (~90 dB), sufficient to image the semi-transparent human cornea, and fast image acquisition rate (≥ 2,400 fps), sufficient to suppress most involuntary eye motion artifacts and allow for contactless, in-vivo imaging of the cellular structure of the human cornea. Volumetric images acquired in-vivo from corneas of healthy subjects show corneal epithelial, endothelial and keratocytes cells, as well as sub-basal and stromal corneal nerves. The system’s high axial resolution also allows for clear identification and morphometry of the corneal endothelium, Descemet’s membrane and the pre-Descemet’s (Dua) layer.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".