Fluid-Structure Interaction and Machine Leaning for Controlling Unruptured Intracranial Aneurysms

Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients.The proposal aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods for fluid-structure interaction modeling (to accurately describe the mechanical exchanges between the blood flow, the surrounding vessel tissue, and the flow-deviator) and deep reinforcement learning algorithms (to identify and to invent a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state).

This has never been done before in this context and should thus open both new theoretical and numerical opportunities.CURE takes the vital steps of bringing novel computational and optimization frameworks to the next level capable of studying the selected flow diverter treatment in order to reduce the risk of hemorrhage in cerebral aneurysms, of supporting the decisions of treatment options by medical doctors and finally of providing guidance in the development of new implant design.

Such unique capabilities can save millions of lives worldwide, improve the life quality of patients, eliminate lifelong side-effects due to sub-optimal treatment planning and delivery; and reduce the tremendous societal and economic burden linked to poor patient outcome.The proposed work has potential to reshape the future of intracranial aneurysm risk management.

It is highly multidisciplinary, and the methods proposed and developed as a part of this research can be quickly adapted to a wide range of engineering and bio-medical applications.