Enhanced depth imaging in swept-source optical coherence tomography: Improving visibility of choroid and sclera, a masked study
Bibliographic record
Abstract
Purpose To compare enhanced depth imaging in swept-source optical coherence tomography and non–enhanced depth imaging optical coherence tomography in their ability to capture choroidal and scleral details. Methods Averaged foveal B-Scans were obtained from 40 eyes of 20 healthy volunteers by swept-source optical coherence tomography with and without enhanced depth imaging. Visibility and contrast of vascular details within the choroid, choroidoscleral junction, and sclera were evaluated by masked readers using an ordinal scoring scale. Outcomes were analyzed using the Wilcoxon signed rank-sum test. Results Visibility of the choroidal vascular details ( Z = 5.94, p < .001), the choroidoscleral junction ( Z = 5.85, p < .001), and the sclera ( Z = 6.80, p < .001) was significantly higher with enhanced depth imaging than with non–enhanced depth imaging swept-source optical coherence tomography. Similarly, image contrast was significantly higher with enhanced depth imaging than with non–enhanced depth imaging swept-source optical coherence tomography for the choroidal vascular details ( Z = 9.47, p < .001), for the choroidoscleral junction ( Z = 9.28, p < .001), and for the sclera ( Z = 9.42, p < .001). Conclusion Enhanced depth imaging applied to swept-source optical coherence tomography–averaged foveal B-scans enhances visualization of the choroidal details, of the choroidoscleral junction, and of the sclera. This novel modality can easily be implemented in clinics and could improve our understanding of conditions involving the choroid or the sclera.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".