Background Bleb presence in intracranial aneurysms (IAs) is a known indication of instability and vulnerability.
Objective To develop and evaluate predictive models of bleb development in IAs based on hemodynamics, geometry, anatomical location, and patient population.
Methods Cross-sectional data (one time point) of 2395 IAs were used for training bleb formation models using machine learning (random forest, support vector machine, logistic regression, k-nearest neighbor, and bagging). Aneurysm hemodynamics and geometry were characterized using image-based computational fluid dynamics. A separate dataset with 266 aneurysms was used for model evaluation. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), true positive rate (TPR), false positive rate (FPR), precision, and balanced accuracy.
Results The final model retained 18 variables, including hemodynamic, geometrical, location, multiplicity, and morphology parameters, and patient population. Generally, strong and concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns together with larger, more elongated, and more distorted shapes were associated with bleb formation. The best performance on the validation set was achieved by the random forest model (AUC=0.82, TPR=91%, FPR=36%, misclassification error=27%).
Conclusions Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.
- blood flow
Data availability statement
Data are available upon reasonable request.
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Contributors SFSA and JRC designed the study. BC, AKY, TGW, HHW, MJK, SA-H, FTC, BRJ, MN, TK, JF, and YT contributed to data collection. SFSA and FM contributed to development of the methodology. FM and JRC designed the software tools. SFSA and JRC identified blebs in the dataset. SFSA curated the data. SFSA and MS performed the data analysis. SFSA, FM, MS, and JRC contributed to the interpretation of the results. AMR and JRC acquired funding, supervised students, and coordinated the project. SFSA and JRC drafted the manuscript. All authors contributed to the manuscript edition and approved the final manuscript. JRC acted as guarantor.
Funding This work was supported by NIH grant R01NS097457.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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