Automatic C-arm Positioning Using Multi-Functional User Interface
Bibliographic record
Abstract
C-arm positioning is a critical step of the surgical workflow. The traditional method is often time consuming and results in additional radiation exposure to the patient and surgical staff. We propose a user interface that allows surgeons to interact with a simulated X-ray 3D reconstruction of the patient’s anatomy. Optimal views chosen by the surgeon with the simulated X-ray are used to calculate the C-arm position required to achieve that view. The proposed system uses pre-operative CT data to generate a 3D model, and inverse kinematics with 6 degrees of freedom to calculate the C-arm joint parameters. Day of surgery patient position variations are factored in through registration methods using the Kinect. Quantitative results were validated by comparing outputs with ground truths, and results indicate our method can output C-arm position values close to the truth considering the limitation of working with truncated values. Automatic positioning reduces radiation by minimizing typical positioning errors. Future work will include the integration of radiation exposure measurements and visualization into the user interface.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".