The rupture of intracranial aneurysm (IA) is the most common cause of subarachnoid hemorrhage (SAH), resulting in patient death and disability. Recently, the morphology of the aneurysm, wall condition as well as hemodynamic factors were found to have kind of relationship with the stability of the aneurysm. In this project, we extracted clinical characteristics, morphology parameters, wall condition and hemodynamic parameters together to predict aneurysm stability using a machine learning model based on 4D-Flow MRI and black blood MRI. Among the two models, the Support Vector Machines model performed well, and the multi-parameter prediction result was greater than 95%.
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