Endoscopic laser speckle imaging of tissue blood flow: Applications in the human knee
Bibliographic record
Abstract
This work represents the first clinical data acquired with the endoscopic laser speckle imaging (eLSPI) system, a new diagnostic tool developed for real-time imaging of tissue blood flow during endoscopic surgical procedures. eLSPI was used to image tissue perfusion in the medial compartment of the knee of five patients requiring arthroscopic knee surgery. The effectiveness of eLSPI as a diagnostic tool was tested by measuring changes in tissue perfusion resultant from tourniquet application, and intra-articular epinephrine. eLSPI produced real-time perfusion video images of tissue blood flow in the knee joint. Tourniquet applications produced consistent decreases in mean perfusion index measurements (29.3% +/- 5.1% in meniscus; 39.5% +/- 8.2% in synovium with an intra patient variability of 6%-9%). A dose-dependent vasoconstrictive response to the administration of intra-articular epinephrine was measured, with maximum dose producing a mean decrease in perfusion of 31.0%-9.3% in meniscus and 41.2%-10.9% in synovium. eLSPI consistently detects decreases in articular tissue blood flow resultant from tourniquet inflation or from the administration of increasing concentrations of epinephrine. These are the first in vivo results indicating physiologic changes in articular tissue as a function of two commonly applied practices in endoscopic joint surgery.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".