D-13 | Initial Utility Testing of Virtual Reality Modeling for Pre-Procedural Planning in Congenital Cardiac Interventions
Bibliographic record
Abstract
The complexity of procedures performed by congenital interventional cardiologists has increased. As adult structural interventionalists have moved further towards pre-procedural imaging and planning, pediatric interventionalists do not routinely use advanced imaging for this purpose. The need for pre-procedural planning of interventions using advanced imaging by congenital interventionalists to reflect best practices of adult structural interventionalists is as important as ever. Using a novel virtual reality software, Elucis (Realize Medical, Ottawa, ON), a pilot study of ten interventional procedures were planned in a virtual arena and then performed in the cardiac catheterization laboratory. Volumetric data for virtual cardiac segmentation was obtained from computed tomography angiography (CTA). Two transcatheter pulmonary valves, one adaptive pre-stent and transcatheter pulmonary valve, one transcatheter mitral valve, and seven other stenting procedures of pulmonary arteries, coarctation of the aorta, and patent ductus arteriosus were modeled using Elucis and virtual implantations were done. The patients ranged from 5 days to 42 years of age, and all had a CTA done within six months of their procedure. The intended interventions were then performed in the catheterization lab. Each procedure was successful with no major complications. Pre-procedural planning using advanced imaging should be a focus in the next generation of congenital cardiac interventions. The feasibility of procedural planning in a virtual arena has proved helpful in our institution in a variety of different interventions. Future innovation in the field of procedural planning using virtual reality should continue to focus on advancing patient education, improving outcomes, decreasing procedural and fluoroscopic time, and reducing complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.200 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".