Abstract
Abdominal aortic aneurysm is a serious vascular disease, which is the progressive dilation of the abdominal aorta caused by the weakening of the aortic wall. Its rupture has been known as a significant cause of mortality for adults older than sixty-five years of age. Screening and assessment of abdominal aortic aneurysms are currently performed by either ultrasound or computed tomography, with the latter technology being the current gold standard. Each abdominal aortic aneurysm is different having varying percentage of size, thrombus, and calcification. These imaging markers play a critical role in determining rupture risk and therefore management of treatment. We propose here a novel application of a nonlinear dynamical model and stochastic pattern classification of abdominal aortic aneurysm imaging markers for rupture risk prediction on computed tomography scans.