Real‐time guidance of thermal and ultrashort pulsed laser ablation in hard tissue using inline coherent imaging
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: During tissue ablation, laser light can be delivered with high precision in the transverse dimensions but final incision depth can be difficult to control. We monitor incision depth as it progresses, providing feedback to ensure that material removal occurs within a localized target volume, reducing the possibility of undesirable damage to tissues below the incision. MATERIALS AND METHODS: Ex vivo cortical and cancellous bone was ablated using pulsed lasers with center wavelengths of 1,064 and 1,070 nm, while being imaged in real-time using inline coherent imaging (ICI) at rates of up to 300 kHz and axial resolution of ∼6 µm. With real-time feedback, laser exposure was terminated before perforating into natural inclusions of the cancellous bone and verified by brightfield microscopy of the crater cross-sections accessed via side-polishing. RESULTS: ICI provides direct information about incision penetration even in the presence of intense backscatter from the pulsed laser and plasma emissions. In this study, ICI is able to anticipate structures 176 ± 8 µm below the ablation front with signal intensity 9 ± 2 dB above the noise floor. As a result, the operator is able to terminate exposure of the laser sparing a 50 µm thick layer of bone between the bottom of the incision to a natural inclusion in the cancellous bone. Versatility of the ICI system was demonstrated over a wide range of light-tissue interactions from thermal regime to direct solid-plasma transition. CONCLUSIONS: ICI can be used as non-contact real-time feedback to monitor the depth of an incision created by laser ablation, especially in heterogeneous tissue where ablation rate is less predictable. Furthermore, ICI can image below the ablation front making it possible to stop laser exposure to limit unintentional damage to subsurface structures such as blood vessels or nervous tissue.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 it