Simultaneous Investigation of Vascular and Retinal Pigment Epithelial Pathologies of Exudative Macular Diseases by Multifunctional Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To investigate exudative macular disease, multifunctional optical coherence tomography (MF-OCT) using a 1-μm probe band was developed. The clinical utility of MF-OCT was examined in a descriptive case series. METHODS: Ten eyes of nine subjects with exudative macular disease, including one eye with age-related macular degeneration (AMD), one eye with idiopathic neovascular maculopathy, and eight eyes with polypoidal choroidal vasculopathy (PCV), were investigated. Areas of 6 × 6 mm(2) around the pathologic region were scanned with 512 × 1024 depth scans in 6.6 seconds. Structural OCT, Doppler optical coherence angiography (OCA), and cumulative phase retardation images were obtained with a single measurement. Each MF-OCT image visualized the structure, vasculature, and birefringence. Degree of polarization uniformity values were also obtained for selective visualization of the retinal pigment epithelium (RPE). The MF-OCT images were compared with conventional ophthalmic images. RESULTS: Abnormal vasculatures were observed with Doppler OCA in all eyes, which presented high similarity to indocyanine green angiography in the midphase. The RPE and exudation in the pathologic regions were discriminated in one eye with AMD and five of eight eyes with PCV. Cumulative phase retardation visualized fibrosis scars in two of the PCV cases. CONCLUSIONS: Multifunctional OCT revealed depth-resolved abnormal vasculatures, the integrity of the RPE and choroid, discrimination of the RPE and exudation, and existence of fibrosis scars in exudative macular diseases. Interpretation of MF-OCT examination is well matched with conventional ophthalmic examination. These results suggest that MF-OCT can be used as a noninvasive ophthalmic examination tool prior to conventional examinations in clinical routines.
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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 | 0.002 |
| 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 | 0.001 |
| 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".