Conference Agenda

Arterial bifurcations are common sites of cerebral aneurysm development, largely because of the complex hemodynamic environment generated by flow division. However, the relationship between bifurcation geometry and aneurysm formation, growth, or rupture remains incompletely defined. This study aimed to evaluate the association between quantitative aneurysm morphology and bifurcation geometry, which was defined as the combined spatial arrangement of branch angles and vessel diameters.

A total of 197 bifurcation cerebral aneurysms were analyzed and for each case, three-dimensional vascular reconstructions were used to extract aneurysm morphological parameters and bifurcation-related geometric features. Branch angles and vessel diameters were measured using a systematic centerline-based approach. A regression-based machine learning framework was then applied to test different predictive models and to assess the relationship between bifurcation geometry and aneurysm morphology. Ridge regression was selected as the final model because it provided the most stable balance between predictive performance and interpretability.

Among the models tested for the different aneurysm morphological variables, the model predicting neck diameter achieved the best predictive performance, as measured by the coefficient of determination. Daughter-vessel diameters were the strongest predictors, whereas angular variables alone showed only limited predictive value. In the separate feature-set analysis, diameter-based models explained substantially more variance than angle-based models. However, the combined angle-diameter model provided the most informative representa-tion of the local bifurcation configuration. These findings suggest that bifurcation geometry should be interpreted as an integrated angle-diameter configuration rather than as an isolated angular feature.