Admittance‐Controlled Robotic Assistant for Fibula Osteotomies in Mandible Reconstruction Surgery
Bibliographic record
Abstract
Herein, a semiautonomous robot control system for mandible reconstruction surgery is proposed. To reconstruct a segmental defect of the mandible caused by cancerous tissue, a piece of matched fibula bone is often segmented and used to replace the removed mandible section. Herein, to provide guidance to the surgeon during fibula segmentation according to the reconstruction surgical plan and improve the fibula bone cutting accuracy, an admittance‐controlled robotic assistant incorporating 3D augmented reality (AR) visualization and haptic virtual fixtures (VFs) is proposed. The admittance controller is used to reduce the surgeon's hand tremor. VF and AR are used to provide haptic and visual guidance to the surgeon, respectively. A feasibility study is conducted through a comparison of fibula osteotomies when performed with image‐guided surgery, AR‐guided surgery, VF‐guided robot‐assisted surgery, and AR‐ and VF‐guided robot‐assisted surgery. Experimental results show the effectiveness of the proposed admittance‐controlled robotic assistant with AR and VF compared with the other three methods. The proposed method is found to be able to increase precision of the osteotomized segments with a lower average linear variation of 1.04 ± 0.79 mm and a lower average angular variation of 1.83 ± 1.85° compared with the virtual preoperative plan.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".