Guidance of hard tissue ablation by forward-viewing optical coherence tomography
Bibliographic record
Abstract
A key issue in laser surgery is the inability for the human operator to stop the laser irradiation in time while cutting/ablating delicate tissue layers. In the present work, we forward-image through the laser machining front in complex biological tissue (dense bovine bone) to monitor the incision's approach to subsurface interfaces in real-time (47-312 kHz line rate). Feedback from imaging is used to stop the drilling process within 150 micron of a targeted interface. This is accomplished by combining the high temporal and spatial resolution of infrared optical coherence tomography (OCT) with a robust, turn-key, high brightness fiber laser. The high sensitivity of the imaging system (~100 dB) permit imaging through the rapidly changing beam path even with the additional scattering caused by the thermal cutting process. In spectral-domain OCT, the imaging acquisition period is easily locked to the machining laser exposure. Though motion-induced artifacts reduce interface contrast, they do not introduce incorrect depth measurements as found in other OCT variants. Standard tomography imaging of the tissue (B-scans) is also recorded in situ before and after laser processing to highlight morphology changes.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".