Single-Shot Ultra-Widefield Polarization-Diversity Optical Coherence Tomography for Assessing Retinal and Choroidal Pathologies
Bibliographic record
Abstract
Background: Optical coherence tomography (OCT) is a leading ocular imaging modality, known for delivering high-resolution volumetric morphological images. However, conventional OCT systems are limited by their narrow field-of-view (FOV) and their reliance on scattering contrast, lacking molecular specificity. Methods: To address these limitations, we developed a custom-built 105∘ ultra-widefield polarization-diversity OCT (UWF PD-OCT) system for assessing various retinal and choroidal conditions, which is particularly advantageous for visualizing peripheral retinal abnormalities. Patients with peripheral lesions or pigmentary changes were imaged using the UWF PD-OCT to evaluate the system’s diagnostic capabilities. Comparisons were made with conventional swept-source OCT and other standard clinical imaging modalities to highlight the benefits of depolarization contrast for identifying pathological changes. Results: The molecular-specific contrast offered by UWF PD-OCT enhanced the detection of disease-specific features, particularly in the peripheral retina, by capturing melanin distribution and pigmentary changes in a single shot. This detailed visualization allows clinicians to monitor disease progression with greater precision, offering more accurate insights into retinal and choroidal pathologies. Conclusions: Integrating UWF PD-OCT into clinical practice represents a major advancement in ocular imaging, enabling comprehensive views of retinal pathologies that are difficult to capture with current modalities. This technology holds great potential to transform the diagnosis and management of retinal and choroidal diseases by providing unique insights into peripheral retinal abnormalities and melanin-specific changes, critical for early detection and timely intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".