Artificial X-ray imaging system (AXIS) – design and evaluation on C-arm performance in operating room and educational settings
Bibliographic record
Abstract
Fluoroscopic C-arms are operated by medical radiography technologists (MRTs) in Canadian operating rooms (ORs). Newly trained MRTs often experience most of their practical learning curve with C-arms in the OR, where achieving the radiographic views requested by surgeons can be challenging. New MRTs often require several scout X-rays during C-arm positioning, resulting in unnecessary radiation exposure and added OR time. To address this problem we have designed an Artificial X-ray Imaging System (AXIS) in order to assess the utility of artificial X-rays in improving the C-arm positioning performance by inexperienced users. AXIS is designed to generate Digitally Reconstructed Radiographs (DRRs), or artificial X-ray images, based on the relative position of a C-arm and manikin. We enrolled 30 participants into our user study, each of whom performed four activities: an introduction session, an AXIS-guided evaluation, a non-AXIS-guided evaluation, and a questionnaire. The main goal of the study was to compare C-arm positioning performance with and without AXIS guidance. For each evaluation, the participants had to replicate a set of target X-ray images by taking real radiographs of the manikin with the C-arm. During the AXIS evaluation, artificial X-rays were generated at 2 Hz for guidance, while in the non-AXIS evaluation, the participants had to acquire real X-rays to guide them toward the correct view. We recorded the number of real X-rays and time required per task, as well as tracked the C-arm’s pose and compared it to the target pose to determine positioning accuracy. We found that users required 53% fewer scout X-rays and achieved 10% better C-arm displacement accuracies when guided by AXIS, without requiring more time to complete the imaging tasks. From the questionnaires we found that, on average, participants felt significantly more confident in their ability to capture correct anatomical views when they were guided by AXIS. Moreover, the participants found the usefulness of AXIS in guiding them to the desired view to be ‘very good’. Overall, we are encouraged by these findings and plan to further develop this system with the goal of deploying it both for training and intraoperative uses.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".