Cerebral Aneurysmal Coiling in Virtual Reality ‐ An Overview of Novice Skill Development
Bibliographic record
Abstract
Introduction and Aims Cerebral endovascular aneurysm coiling is an interventional angiography technique which requires the use of fine motor and visuo‐spatial skills in order to guide tools throughout relevant vascular anatomy and deposit coils within the aneurysm. With the help of quantitatively limited fluoroscopy, coils are established through microcatheter placement in the aneurysm neck and electrolytically detached after their appropriate rotation and positioning. This procedure requires, and is currently limited to, extensive clinical training in the AngioSuite, where trainees are closely supervised by a consultant. This approach, although providing the current gold standard in training, limits the pace of skill development, exposes patients to unnecessary risk and wastes valuable monetary and personnel resources. The development of the skills needed for these procedures can be facilitated by 3D simulator‐based training. Simulation‐based medical education has recently started focusing on personalized training in reducing errors and development of specific skills. The ANGIO Mentor visual‐haptic simulator has been regarded as an effective training tool, increasing performance in diagnostic cerebral angiography, however, this simulator has not been tested thoroughly in its ability to train interventional skills. In particular, the details of the aneurysm coiling process during simulation‐based training have not been assessed. Methods In this study, 12 novice medical students were given simulation‐based diagnostic cerebral angiography training until a procedural plateau in performance, established in our previous work (Zaika et al., 2016). Subsequently, they were trained using video tutorials and written instructions to identify, measure and intervene with cerebral aneurysms using endovascular coils. Over the span of 6 sessions, participants were assessed on their procedural pace, coiling quantity and quality, and perforation rates. Concurrently, their spatial ability was assessed using a mental rotations test (MRT) and used in the performance analysis. Results and Discussion We found that all individuals were able to perform the procedure faster after 6 sessions, reducing their average time from 42 to 24 minutes. Coil success rate improved over from 82% to 88% and coil packing rate remained consistent at 30% throughout testing. High perforation rate seen at the start of the study showed a trend of decreasing over the latter sessions, however, over half of aneurysms were still being perforated by the novice participants. No change in aneurysm coiling quality was found, with a slight decrease in number of parent artery coil protrusions. High MRT individuals were also better able to establish guider catheter and associate tools prior to coiling. This work identifies the utility of simulation‐based cerebral angiography training in identifying the particular difficulties trainees experience in learning procedural skills, including prevention of perforations, proper positioning and success of coils within the aneurysm. Support or Funding Information This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".