Evaluation of the Clinical Application of Multi-Color Optical Coherence Tomography as a Diagnostic Tool for Different Retinal Pathologies
Bibliographic record
Abstract
Objective: To assess clinical application of multi-color OCT (Optical Coherence Tomography) with the utilization of the CSLO (Confocal Scanning Laser-Ophthalmoscopy) in various pathologies of retina. Methodology: This study was conducted at in Benazir Bhutto Shaheed Teaching Hospital Abbottabad and the duration of this study was from April 2021 to June 2021. There were thirty-six patients in this research study who were suffering from various retinal pathologies as disorders of vitreomacular interface, diabetic retinopathy and macular degeneration related with age with the utilization of multi-color OCT as a tool of screening. Results: This study discovered that ophthalmologist were able to get high resolution images of CSLO reflectance because of the automatic tracking system of this particular tool (new version). Confocal optics may be used to avoid the scattering of light. Some of the differences were presence of hemorrhages and presences of pigment changes when comparison with the conventional CFP was performed. Approximately 20.0% patients with AMD, 37.50% patients with diabetes and 100% patients suffering from disorders of vitreomacular interface could have been missed easily with the utilization of CFP. Conclusions: The findings conclude that multi-color OCT is able to deliver information & figures far more confident was compared to conventional method of CFP, because it is much influenced by the media opacities. For the best interpretation of the Multi-color OCT, more watchfulness of the ophthalmologists is necessary with high level of comprehensiveness. Keywords: Retinal Pathologies, Multi-Color, Diabetic Retinopathy, Optical Coherence Tomography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".