Real-time co-localized OCT surveillance of laser therapy using motion corrected speckle decorrelation
Bibliographic record
Abstract
We present a system capable of real-time delivery and monitoring of laser therapy by imaging with optical coherence tomography (OCT) through a double-clad fiber (DCF). A double-clad fiber coupler is used to inject and collect OCT light into the core of a DCF and inject the therapy light into its larger inner cladding, allowing for both imaging and therapy to be perfectly coregistered. Monitoring of treatment depth is achieved by calculating the speckle intensity decorrelation occurring during tissue coagulation. Furthermore, an analytical noise correction was used on the correlation to extend the maximum monitoring depth. We also present a method for correcting motion-induced decorrelation using a lookup table. Using the value of the noise- and motion-corrected correlation coefficient in a novel approach, our system is capable of identifying the depth of thermal coagulation in real time and automatically shut the therapy laser off when the targeted depth is reached. The process is demonstrated ex vivo in rat tongue and abdominal muscles for depths ranging from 500 µm to 1000 µm with induced motion in real time.
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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 | 0.000 | 0.001 |
| 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 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".