Computer Integrated Surgical Systems and Technology ERC
Bibliographic record
Abstract
Use of active, optical tracking Surgical Guidance systems provides line of sight problems to the surgeon. We plan to use a new magnetic system, 'Aurora ' from Northern Digital Inc. (Canada) and Mednetix AG (Switzerland), in intra-operative fluoroscopy to develop an integrated system for surgical guidance. Here we outline the modules developed for use with this system, including a novel registration method. 1. Summary Intra-operative fluoroscopy is useful in many percutaneous procedures and spinal surgeries, such as bone biopsy and vertebroplasty. Drawbacks, however, include exposing the surgeon to repeated doses of radiation, obstructing access with the C-arm in the operating area, and the limited number of views available. A possible solution to these problems is 'virtual fluoroscopy', where an initial fluoroscopic image is captured and a representation of a (tracked) instrument is projected onto the image. This reduces the amount of exposure to radiation during the procedure, allows multiple views and reduces the amount of obstruction from the C-arm during surgery. 2. Previous work Existing 2D-3D point registration algorithms rely on the availability of a large number of data points; Iterative Closest Point (ICP) and other statistical approaches do not work well with small data sets owing to local minima. Our system is intended to be used with 4 to 6 markers. A recent, and similar, alternative to ICP is 3PLFLS[1]- we will discuss the differences in our approach later. 3. Workflow The markers will be in view of the C-arm whenever calibration/registration is performed or updated, they could be on the instrument to be artificially rendered itself, or a separate calibration object. •
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.213 | 0.162 |
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".