Ultrawide field, distortion-corrected ocular shape estimation with MHz optical coherence tomography (OCT)
Bibliographic record
Abstract
Ocular deformation may be associated with biomechanical alterations in the structures of the eye, especially the cornea and sclera in conditions such as keratoconus, congenital glaucoma, and pathological myopia. Here, we propose a method to estimate ocular shape using an ultra-wide field MHz swept-source optical coherence tomography (SS-OCT) with a Fourier Domain Mode-Locked (FDML) laser and distortion correction of the images. The ocular biometrics for distortion correction was collected by an IOLMaster 700, and localized Gaussian curvature was proposed to quantify the ocular curvature covering a field-of-view up to 65°×62°. We achieved repeatable curvature shape measurements (intraclass coefficient = 0.88 ± 0.06) and demonstrated its applicability in a pilot study with individuals (N = 11) with various degrees of myopia.
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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 | 0.001 |
| 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) | 0.001 | 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".