Development and evaluation of an open-source virtual reality C-Arm simulator
Bibliographic record
Abstract
C-Arm positioning for interventional spine procedures is often associated with a steep learning curve. This task requires mentally reconstructing 3D surgical tools and patient anatomy from a 2D X-ray image, which is non-trivial and acquired through years of experience. Standard training via apprenticeship-based programs must be limited due to the unnecessary exposure to ionizing radiation. To this end, we propose a Virtual Reality C-Arm simulator for interventional spine procedure training. We implemented the simulator as an open-source module in Slicer, and evaluated its efficacy through a user study, recruiting medical residents and expert clinicians. Users showed an overall significant improvement in C-Arm placement with regards to angular accuracy (mean ~2 degree improvement), and total procedure time (mean 11 minutes less time). The face and content validity was evaluated positively through a Likert scale questionnaire, with a mean score of 4 (out of 5) or higher for each of the questions. The results show the simulator provides effective training for C-Arm positioning, while eliminating the exposure to ionizing radiation associated with the current training standard. Although this work is catered towards spinal procedures, the system is extendable to other fields, such as cardiac and orthopaedic, and will be explored in future works.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".