Imaging of Ocular Angle Structures with Fourier Domain Optical Coherence Tomography
Bibliographic record
Abstract
BACKGROUND: Imaging of the Schlemm's Canal is complicated by the small physiological size and the location several hundred microns beneath the sclera. Noninvasive imaging of Schlemm's canal and Trabecular Meshwork (TM) in vivo with Fourier Domain Optical Coherence Tomography (FD OCT) can provide clinicians with a powerful tool to visualize ocular angular structures crucial for glaucoma management. PURPOSE: To investigate the appearance of Schlemm's canal and TM on FD OCT images. METHODS: FD OCT images of the Schlemm's canal and TM were obtained with three different wavelengths using prototype FD OCT systems in a normal volunteer. FD OCT images using the 1310 nm wavelength prototype were obtained in three representative cases of glaucoma surgery performed on angle structures. RESULTS: The longer imaging depth and deeper tissue penetration of the 1310 nm system provided the clearest image of the TM and Schlemm's canal in the normal patient. In case 1, images pre- and post-trabectome surgery clearly showed the location and appearance of TM. In case 2, images post-canaloplasty surgery showed the location and appearance of Schlemm's canal. In case 3, images pre- and post-trabeculotomy surgery further confirms the appearance and location of the Schlemm's canal and TM. CONCLUSION: Operating wavelength of the FD OCT system and exact location of the scan across different meridians minimally affects the appearance of the ocular anatomy. The postoperative images of three angle glaucoma surgeries confirmed the location of Schlemm's canal and TM. How to cite this article: Asrani S, Young M, Xu J, Sarunic MV. Imaging of Ocular Angle Structures with Fourier Domain Optical Coherence Tomography. J Current Glau Prac 2013;7(2):85-87.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".