  The two most important aneurysm types are cerebral aneurysms (CA) and
abdominal aortic aneurysms (AAA), accounting together for over 80\% of all
fatal aneurysm incidences. To minimise aneurysm related deaths, clinicians
require various tools to accurately estimate its rupture risk. For both
aneurysm types, the current state-of-the-art tools to evaluate rupture risk are
identified and evaluated in terms of clinical applicability. We perform a
comprehensive literature review, using the Web of Science database. Identified
records (3127) are clustered by modelling approach and aneurysm location in a
meta-analysis to quantify scientific relevance and to extract modelling
patterns and further assessed according to PRISMA guidelines (179 full text
screens). Beside general differences and similarities of CA and AAA, we
identify and systematically evaluate four major modelling approaches on
aneurysm rupture risk: finite element analysis and computational fluid dynamics
as deterministic approaches and machine learning and assessment-tools and
dimensionless parameters as stochastic approaches. The latter score highest in
the evaluation for their potential as clinical applications for rupture
prediction, due to readiness level and user friendliness. Deterministic
approaches are less likely to be applied in a clinical environment because of
their high model complexity. Because deterministic approaches consider
underlying mechanism for aneurysm rupture, they have improved capability to
account for unusual patient-specific characteristics, compared to stochastic
approaches. 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 accessibility for clinicians and
prediction quality for rupture risk.
