Development of an Ultrasonic Bone Cutting Instrument for Robotic-assisted Surgery
Bibliographic record
Abstract
The use of robotic assistance in abdominal surgeries has increased rapidly in the last two decades and da Vinci Surgical System (dVSS) variants by Intuitive Surgical are now used in over one million procedures annually. With the widespread availability of the dVSS and the release of competing teleoperated systems anticipated in the near future, there is motivation to investigate whether non-abdominal operations may also benefit from the advantages provided by these robotics systems. Several research groups, in early feasibility studies, have found benefits of using the dVSS for operations in which hard tissue removal is required, including transoral, orthopaedics, spinal, and neurosurgery. However, the application of the robot in craniofacial procedures remains unexplored. It is hypothesized that the robotic-assisted system, with its improved precision, motion scaling, and advanced visualization, may similarly provide benefits in both minimally invasive and open cases of craniofacial surgery. However, the lack of robotic instruments for hard tissue precludes exploration in craniofacial procedures and remains a blocker for evaluating a fully robotic approach in operations where any hard tissue removal is required. The research presented here intends to address this limitation by developing a ultrasonic bone cutting instrument for the dVSS, and then evaluating the initial feasibility of using the system for craniofacial operations by performing a simulated procedure. To develop the prototype, performance and clinical requirements were first established and the instrument was designed and optimized using finite element analysis and a commercially available multi-objective genetic algorithm. The prototype was then verified using a combination of amplitude characterization, fatigue testing, and experimental modal analysis. Benchtop testing indicated the optimal cutting approach based on cutting depth, speed, and approach angle, and teleoperated cutting tests were performed on representative craniofacial models using an open approach. Initial experiments show that teleoperated bone cutting using an ultrasonic tool is feasible despite the lack of force feedback provided by the robot, and results indicate that there may be improvements to accuracy over manual tools. Further testing in a minimally invasive model with additional participants is required to evaluate robot setup, reach and range of motion of the prototype instrument, operating time, and safety
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".