Disablement of a surgical drill via CT guidance to protect vital anatomy
Bibliographic record
Abstract
Applying image-guidance to an electronically-controlled surgical drill can prevent damage to patients' anatomy during resection. A system is presented that disables the drill when it nears pre-defined critical patient anatomy. The system consists of a tracking system, image-guidance software, and drill-control circuit. The software was developed in C++ with the help of the Image-Guided Surgery Toolkit, and was designed to track tools based on input from a MicronTracker (Claron Tech, Toronto, Ontario) tracking system. The system registers physical to image space using fiducial markers rigidly attached to the patient, tracks the drill, and automatically disables the drill when close to restricted regions. A coordinate reference frame is used for all physical acquisitions. Visual feedback of the tool's position in image space is provided during tracking. Two tests were performed to determine the feasibility of the system. Virtual restricted regions were defined inside a phantom, and an operator attempted to drill the phantom with the help of the application. No feedback was provided to the user except for the automatic disablement of the drill by the application when close to a restricted region. In the first test, the drill was disabled at 0.74 ± 0.46 mm from the restricted region and entered 5.3% of the surface area of the restricted region. In the second test, the drill was disabled 1.3 ± 0.69 mm from the restricted region and entered the restricted region 8.5% of the time. We conclude that the system shows promise and further testing should be conducted.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".