Abstract

AAA is a serious condition in which the size of the abdominal aorta starts to increase and can eventually lead to rupture. In the last decade, the UK has witnessed an average of 3000 AAA -related deaths annually, equating to approximately one death every three hours. Globally, the average was estimated to exceed 150,000 deaths per year over the last decade, which translates to about one death every three minutes. Understanding the growth of an aortic aneurysm and developing personalised assessments has been classified as one of the top three priorities by the Vascular Society of Great Britain and Ireland. In our work, we explore the use of high-fidelity Computational Fluid Dynamics (CFD) to predict wall shear stress (WSS). However, WSS can only be derived for timescales of 1-2 cardiac cycles, whereas appreciable growth occurs over months. We employ solid mechanics growth models in conjunction with uncertainty quantification (UQ) to couple these timescales and predict probabilistic growth scenarios for abdominal aortic aneurysms. Additionally, we delve into the potential applications of machine learning for predicting WSS and growth based on the geometry.