Virtual-versus-Real Implantation of Flow Diverters: Clinical Potential and Influence of Vascular Geometry
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intracranial stents have become extremely important in the endovascular management of complex intracranial aneurysms. Sizing and landing zone predictions are still very challenging steps in the procedure. Virtual stent deployment may help therapeutic planning, device choice, and hemodynamic simulations. We aimed to assess the predictability of our recently developed virtual deployment model by comparing in vivo and virtual stents implanted in a consecutive series of patients presenting with intracranial aneurysms. MATERIALS AND METHODS: Virtual stents were implanted in patient-specific geometries of intracranial aneurysms treated with the Pipeline Embolization Device. The length and cross-section of virtual and real stents measured with conebeam CT were compared. The influence of vessel geometry modifications occurring during the intervention was analyzed. RESULTS: = 0.67) with the virtual-versus-real stent radius differences of -6% ± 7% (5% ± 4%) for predictions based on pre- and poststent implantation 3D rotational angiography. These mismatches were due principally to implantation concerns and vessel-shape modifications. CONCLUSIONS: The recently proposed geometric model was shown to predict accurately the deployment of Pipeline Embolization Devices when the stent radius was well-assessed. However, unpredictable delivery manipulations and variations of vessel geometry occurring during the intervention might impact the stent implantation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".