Determination of Significant Three-Dimensional Hemodynamic Features for Postembolization Recanalization in Cerebral Aneurysms Through Explainable Artificial Intelligence.

ABSTRACT

BACKGROUND: Recanalization poses challenges after coil embolization in cerebral aneurysms. Establishing predictive models for postembolization recanalization is important for clinical decision making. However, conventional statistical and machine learning (ML) models may overlook critical parameters during the initial selection process. METHODS: In this study, we automated the identification of significant hemodynamic parameters using a PointNet-based deep neural network (DNN), leveraging their three-dimensional spatial features. Further feature analysis was conducted using saliency mapping, an explainable artificial intelligence (XAI) technique. The study encompassed the analysis of velocity, pressure, and wall shear stress in both precoiling and postcoiling models derived from computational fluid dynamics simulations for 58 aneurysms. RESULTS: Velocity was identified as the most pivotal parameter, supported by the lowest P value from statistical analysis and the highest area under the receiver operating characteristic curves/precision-recall curves values from the DNN model. Moreover, visual XAI analysis showed that robust injection flow zones, with notable impingement points in precoiling models, as well as pronounced interplay between flow dynamics and the coiling plane, were important three-dimensional features in identifying the recanalized aneurysms. CONCLUSIONS: The combination of DNN and XAI was found to be an accurate and explainable approach not only at predicting postembolization recanalization but also at discovering unknown features in the future.