Automated 3D bone ablation with 1,070 nm ytterbium‐doped fiber laser enabled by inline coherent imaging
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Laser osteotomy bears well-identified advantages over conventional techniques. However, lack of depth control and collateral thermal damage are barriers to wide clinical implementation. Flexible fiber delivery and economical benefits of ytterbium-doped fiber lasers make them desirable for laser osteotomy. In this work, we demonstrate automated bone ablation with a 1,070 nm industrial-scale fiber laser to create 3D target structures with minimal thermal side-effects. MATERIALS AND METHODS: Fresh and dry ex vivo cortical bone samples are ablated using 50-100 µs laser pulses of 15-30 mJ. In situ inline coherent imaging monitors ablation dynamics with micron precision and on microsecond timescales. Ablation depth is extracted by on-the-fly processing of ICI data, enabling feedback control of depth (via laser pulse number). Final ablated morphology, measured by an ex situ stylus profiler, is compared to the target shape. Histological examination is performed to quantify the thermal side-effects of laser ablation. RESULTS: Percussion drilled hole depth is highly variable for fixed laser parameters (880 ± 151 µm on fresh bone and 1038 ± 148 µm on dry bone) due to nondeterministic ablation. ICI-enabled depth control is implemented to achieve precise ablation of complex 3D features. The RMS deviation between target and ablated morphology is 12.6 µm. The heat-affected zone is found to be 5-10 µm on fresh and dry bone. CONCLUSIONS: An ytterbium-doped fiber laser is utilized for cortical bone ablation with limited thermal side-effects. In situ real-time ICI measurement enables characterization of bone ablation dynamics. Furthermore, ICI closed-loop feedback realizes depth-controlled ablation on heterogeneous bone. This proof-of-principle study shows great promise for ICI-guided laser osteotomy.
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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 | 0.000 |
| 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) | 0.001 | 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".