AB091. A novel method for the measurement of pulsatile choroidal blood flow based on video-rate OCT imaging and automated segmentation of the choroid: description and reproducibility
Bibliographic record
Abstract
Background: Over the years, a variety of non-invasive techniques have been developed to allow the measurement of blood flow in living human eyes. However, none of the existing techniques has yet been adopted in clinical practice due to their limitations and lack of standardization. Moreover, no reliable technique is currently available to measure the pulsatile choroidal blood flow (PCBF). We propose a novel method based on video-rate optical coherence tomography (OCT) imaging and automated segmentation to measure the pulsatile component of choroidal blood flow in vivo, and demonstrate its repeatability. Methods: Adapted from our earlier work (Beaton et al.), this method uses video-rate OCT with enhanced depth imaging and automated segmentation of the choroid to measure the pulsatile choroidal volume change. Imaging is carried out at the fundus for less than a minute at 7 Hz. In each frame, choroidal thickness (CT) is measured by a segmentation algorithm based on graph cuts using an edge-probability weighting scheme. The algorithm computes the CT change corresponding to choroidal filling over the time-series and subsequently derives the pulsatile choroidal volume change through an approximate model of the eye. Fifty-eight subjects were recruited from the Maisonneuve-Rosemont Hospital and PCBF was measured twice in one eye within the same session and by a single examiner. Repeatability was assessed using the Bland-Altman plot and Intraclass correlation coefficient as calculated with SPSS. Results: Two measurements of PCBF were successfully obtained for each eye using our technique. The average measures ICC for choroidal volume change was 0.929 (95% CI, 0.881, 0.958), showing good to excellent repeatability. The Bland-Altman plot and Pearson coefficient (r=0.840, P<0.001) showed agreement and a strong correlation respectively between intra-session measurement of OR in all examined eyes. Conclusions: This study confirms the high repeatability of pulsatile choroidal blood flow measurements obtained with our optical method, allowing further investigation of blood flow in ocular diseases such as glaucoma and AMD.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".