Virtual stenting of intracranial aneurysms: A pilot study for the prediction of treatment success based on hemodynamic simulations
Bibliographic record
Abstract
Endovascular treatment of intracranial aneurysms using flow-diverting devices has revolutionized the treatment of large and complex lesions due to its minimally invasive nature and potential clinical outcomes. However, incomplete or delayed occlusion and persistent intracranial aneurysm growth are still an issue for up to one-third of the patients. We evaluated two patients with intracranial aneurysm located at the internal carotid artery who were treated with flow-diverting devices and had opposite outcomes. Both patients presented with similar aneurysms and were treated with the same device, but after a 1-year follow-up, one case presented with complete occlusion (Case 1) and the other required further treatment (Case 2). To reproduce the interventions, virtual stents were deployed and blood flow simulations were carried out using the respective patient-specific geometries. Afterward, hemodynamic metrics such as aneurysmal inflow reduction, wall shear stresses, oscillatory shear, and inflow concentration indices were quantified. The hemodynamic simulations reveal that for both cases, the neck inflow was clearly reduced due to the therapy (Case 1: 19%, Case 2: 35%). In addition, relevant hemodynamic parameters such as time-averaged wall shear stress (Case 1: 35.6%, Case 2: 57%) and oscillatory shear (Case 1: 33.1%, Case 2: 26.7%) were decreased considerably. However, although stronger relative reductions occurred in the unsuccessful case, the absolute flow values in the successful case were approximately halved. The findings demonstrate that a high relative effect of endovascular devices is not necessarily associated with the desired treatment outcome. Instead, it appears that a successful intracranial aneurysm therapy requires a certain patient-specific inflow threshold.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".