Towards in vivo laser coagulation and concurrent optical coherence tomography through double-clad fiber devices
Bibliographic record
Abstract
There is a strong clinical need for an optical coherence tomography (OCT) system capable of delivering concurrent coagulation light enabling image-guided dynamic laser marking for targeted collection of biopsies, as opposed to a random sampling, to reduce false-negative findings. Here, we present a system based on double-clad fiber (DCF) capable of delivering pulsed laser light through the inner cladding while performing OCT through the core. A previously clinically validated commercial OCT system (NVisionVLE, Ninepoint Medical) was adapted to enable in vivo esophageal image-guided dynamic laser marking. An optimized DCF coupler was implemented into the system to couple both modalities into the DCF. A DCF-based rotary joint was used to couple light to the spinning DCF-based catheter for helical scanning. DCF-based OCT catheters, providing a beam waist diameter of 62μm at a working distance of 9.3mm, for use with a 17-mm diameter balloon sheath, were used for ex vivo imaging of a swine esophagus. Imaging results using the DCF-based clinical system show an image quality comparable with a conventional system with minimal crosstalk-induced artifacts. To further optimize DCF catheter optical design in order to achieve single-pulse marking, a Zemax model of the DCF output and its validation are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".