Comparison of existing aneurysm models and their path forward

Abstract The two most important aneurysm types are cerebral aneurysms and abdominal aortic aneurysms, accounting together for over 80% of all fatal aneurysm incidences. To minimise aneurysm-related deaths, clinicians need several tools to accurately assess the risk of rupture. For both aneurysm types the current state-of-the-art tools to evaluate rupture risks are identified and evaluated in terms of clinical applicability. We perform a comprehensive literature review, using Web-of-Science. The identified records (3127) are clustered by their modeling approach and aneurysm location in a meta-analysis to quantify scientific relevance of the respective approach and to extract modeling patterns. We further assess the different modeling approaches according to PRISMA guidelines (179 full text screens). We identify and systematically evaluate four major modeling approaches on aneurysm rupture risk: (i) finite element analysis and (ii) computational fluid dynamics as deterministic approaches and (iii) machine learning and (iv) assessment-tools as stochastic approaches. In doing so, we evaluated ten approaches for their applicability in clinical practice, cost, accuracy, and other characteristics. Assessment-tools receive the highest score in the evaluation of their potential as a clinical application for rupture prediction due to their readiness level and user friendliness. Deterministic approaches are less likely to be applied in a clinical environment because of their high model complexity. This is mainly due to the fact that for a very accurate simulation the aneurysm behaviour, detailed assumptions are necessary, which physicians often don’t have time to reflect on. However, since deterministic approaches consider underlying mechanisms for aneurysm rupture, they have improved capability to account for unusual patient-specific characteristics, compared to stochastic approaches. In addition to the specific evaluation of current models to determine the rupture probability of aneurysms, we show that an increased interdisciplinary exchange between specialists can boost comprehension of this disease to design tools for a clinical environment. By combining deterministic and stochastic models, advantages of both approaches can improve the accessibility for clinicians and prediction quality for rupture risk.