S15-02 SESSION 15: PLANNING/IMAGING - PART I DEVELOPMENT OF BONE CUTTING INSTRUMENTATION IN THE APPLICATION OF ROBOTIC ASSISTED CRANIOSYNOSTOSIS SURGERY USING THE DA VINCI PLATFORM.
Bibliographic record
Abstract
Introduction: The aim of this project was to develop a bone cutting tool for the da Vinci robotic platform with application in craniofacial procedures for the correction of craniosynostosis with advantages of enhanced surgeon ergonomics, improved bone cutting precision, visualization and minimal access surgery. Methods: In order to assess the feasibility of a robotic-assisted approach, a custom ultrasonic tool was developed to address the unique challenges of accessing the required cut locations on the skull using the remote-center-of-motion configuration of the da Vinci Si system. To address these limitations, new blade geometry was developed to allow for cutting at any angle of attack using a genetic shape optimization algorithm in finite element software. The tool was also designed to maximize length and minimize diameter to be suitable for minimally invasive access. Over 200 iterations of blade design, with cutting up to 4 mm and a 0.5 mm kerf were assessed. The error between finite element model predictions and testing averaged 1% (for prediction of shape and location of modal shapes). Results: A custom ultrasonic tool was developed to address the unique challenges of accessing the required cut locations on the skull using the remote-center-of-motion configuration of the da Vinci Si system. A key advantage of the da Vinci system are the highly dexterous wristed instruments; however, it is infeasible to develop a wristed ultrasonic tool due to high cycles and stresses. To address these limitations, new blade geometry was developed to allow for cutting at any angle of attack using a genetic shape optimization algorithm in finite element software. The tool was also designed to maximize length and minimize diameter to be suitable for minimally invasive access. Over 200 iterations of blade design, with cutting up to 4 mm and a 0.5 mm kerf were assessed. The error between finite element model predictions and testing averaged 1% (for prediction of shape and location of modal shapes). Bone substitute and animal bone were used for testing. 6 cuts total each cut 40 mm in length, 3 mm in depth (3 cuts using Bonesaw sample PCF40, 3 cuts using Bonesaw sample PCF50, Bonesaw samples: PCF40 (0.64 g/cc) and PCF50 (0.80 g/cc) were made and analyzed for time per cut (cutting speed), forces on sample (ATI Gamma), cutting motion/method (video recorded) and soft tissue damage (silicon pad). Data to be presented. Conclusion: Preliminary work has demonstrated an effective blade design that is amenable to application in bone cutting using piezo-electric technology using the da Vinci Si platform. Application in a simulated model for endoscopic strip craniectomy will be presented.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".