Cardiovascular diseases are the leading cause of death in the Western world. Reliable biomechanical rupture risk assessment (BRRA) of abdominal aortic aneurysms (AAA) requires accurate constitutive models and geometry. Current models often assume incompressibility and rely on uniaxial data, neglecting biaxial stress states and cyclic loading. We propose a new experimental framework combining radial compression with existing methods to better capture arterial behavior and improve rupture prediction. A validated stochastic model will be developed using a Bayesian machine learning approach and validated on both ex-vivo rupture tests of brachial aneurysms (BAA) and an extended database of AAAs with known outcomes. A key innovation is a novel geometry reconstruction algorithm enabling automated modeling of rare pathologies, currently beyond the reach of neural networks due to limited data. This breakthrough could significantly advance patient-specific BRRA and open new clinical applications.

Keywords
Arterial wall;Vascular Biomechanics;Abdominal Aortic Aneurysm;Arterial rupture;Baysesian modelling;

26-21225S

English

Skácel Pavel, Ing., Ph.D. - principal person responsible

Institute of Solid Mechanics, Mechatronics and Biomechanics
- responsible department (28.3.2025 - not assigned)
Institute of Solid Mechanics, Mechatronics and Biomechanics
- beneficiary (28.3.2025 - not assigned)

Responsibility: Skácel Pavel, Ing., Ph.D.