Retinal angiography with real-time speckle variance optical coherence tomography
Bibliographic record
Abstract
This report describes a novel, non-invasive and label-free optical imaging technique, speckle variance optical coherence tomography (svOCT), for visualising blood flow within human retinal capillary networks. This imaging system uses a custom-built swept source OCT system operating at a line rate of 100 kHz. Real-time processing and visualisation is implemented on a consumer grade graphics processing unit. To investigate the quality of microvascular detail acquired with this device we compared images of human capillary networks acquired with svOCT and fluorescein angiography. We found that the density of capillary microvasculature acquired with this svOCT device was visibly greater than fluorescein angiography. We also found that this svOCT device had the capacity to generate en face images of distinct capillary networks that are morphologically comparable with previously published histological studies. Finally, we found that this svOCT device has the ability to non-invasively illustrate the common manifestations of diabetic retinopathy and retinal vascular occlusion. The results of this study suggest that graphics processing unit accelerated svOCT has the potential to non-invasively provide useful quantitative information about human retinal capillary networks. Therefore svOCT may have clinical and research applications for the management of retinal microvascular diseases, which are a major cause of visual morbidity worldwide.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".