Towards the Clinical utility of CFD for assessment of intracranial aneurysm rupture – a systematic review and novel parameter-ranking tool
Bibliographic record
Abstract
BACKGROUND: Intracranial aneurysms (IAs) are vascular dilations on cerebral vessels that affect between 1%-5% of the general population, and can cause life-threatening intracranial hemorrhage when ruptured. Computational fluid dynamics (CFD) has emerged as a promising tool to study IAs in recent years, particularly for rupture risk assessment. However, despite dozens of studies, CFD is still far from clinical use due to large variations and frequent contradictions in hemodynamic results between studies. PURPOSE: To identify key gaps in the field of CFD for the study of IA rupture, and to devise a novel tool to rank parameters based on potential clinical utility. METHODS: A Pubmed search identified 231 CFD studies for IAs. Forty-six studies fit our inclusion criteria, with a total of 2791 aneurysms. For included studies, study type, boundary conditions, solver resolutions, parameter definitions, geometric and hemodynamic parameters used, and results found were recorded. DATA SYNTHESIS: Aspect ratio, aneurysm size, low wall shear stress area, average wall shear stress, and size ratio were the parameters that correlate most strongly with IA rupture. LIMITATIONS: Significant differences in parameter definitions, solver spatial and temporal resolutions, number of cycles between studies as well as frequently missing information such as inlet flow rates were identified. A greater emphasis on prospective studies is also needed. CONCLUSIONS: Our recommendations will help increase standardization and bridge the gaps in the CFD community, and expedite the process of making CFD clinically useful in guiding the treatment of IAs.
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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 | 0.024 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".