The Role of Widefield and Ultra Widefield Optical Coherence Tomography in the Diagnosis and Management of Vitreoretinal Diseases
Bibliographic record
Abstract
Background: This study reports on the advantages of wide-field (WF)- and ultra-widefield (UWF)- optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in managing different vitreoretinal diseases in a real-life setting using the new WF—Swept Source (SS)—OCT Xephilio S1 (Canon, Tokyo, Japan). Methods: We conducted an observational retrospective case series study involving 1472 eyes that underwent retinal scans with Canon Xephilio® OCT-S1 between 1 March 2021 and 1 December 2021 at Eyecare Clinic (Brescia, Italy). All patients underwent routine ophthalmologic examinations along with WF and UWF color fundus retinography with Clarus 500™ (Carl Zeiss Meditec, Inc., Dublin, CA, USA) and Xephilio® OCT-S1. WF SS-OCT, UWF-OCT, WF-OCTA, and UWF-OCTA were taken by using Xephilio® OCT-S1. Results: We analyzed 122 peripheral retinal lesions, 144 retinal detachment, 329 high myopic eyes, 37 pediatric cases, 60 vascular retinopathies, 15 choroidal lesions, and 90 eyes as follow-up post vitreoretinal surgery. The OCT-S1 was the only reliable and diagnostic exam for peripheral lesions, pediatric and high myopic cases, and significantly influenced the management in 10% of cases and the postoperative follow-up. Conclusions: WF and UWF OCT and OCTA imaging may help in the management of several vitreoretinal diseases, becoming an indispensable tool for the high-quality management of patients.
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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 | 0.006 |
| 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 | 0.001 | 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".