Comparison of Multicolor Scanning Laser Imaging and Color Fundus Photography in Evaluating Vessel Whitening in Branch Retinal Vein Occlusion
Bibliographic record
Abstract
INTRODUCTION: Few studies have explored MultiColor™ imaging (MCI) in evaluating retinal vascular diseases, particularly branch retinal vein occlusion (BRVO). This study aimed to compare the identification of retinal vessel whitening in BRVO using MCI by scanning confocal laser versus conventional white-flash color fundus photography (CFP). METHODS: Paired images of consecutive patients diagnosed with BRVO who underwent same-day MCI and CFP were reviewed. Visualization of vessel whitening on MCI and CFP was graded and scored using a scale by two masked graders. A longitudinal analysis of the vessel grading score was performed to evaluate the vessel whitening detection by MCI. A correlation analysis was conducted between vessel whitening on MCI and the measured area of retinal ischemia on fluorescein angiography to evaluate the MCI performance. RESULTS: Forty-four eyes of 41 patients (mean age 69 ± 14 years; 61% female) were analyzed. MCI demonstrated superior vessel whitening visibility score than CFP (p < 0.001). Longitudinal analysis showed no significant changes in vessel whitening visibility scores over a mean follow-up time of 430 ± 648 days (p = 0.655). There was a significantly positive correlation between the grading score of vessels whitening by MCI and the area of ischemia by fluorescein angiography (r2 = 0.15; p = 0.036). CONCLUSION: MCI appears to provide a superior detection of whitening BRVO compared to CFP, serving as a rapid and non-invasive correlate of retinal ischemia.
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 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 | 0.003 | 0.006 |
| 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 | 0.001 |
| 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".