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

Introduction

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. This development aims to support decision-making and assessment of treatment options by medical doctors, as well as to improve the life quality and expectancy of patients.

Proposal Overview

The proposal aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework. This framework combines:

1. State-of-the-art numerical methods for fluid-structure interaction modeling, which accurately describes the mechanical exchanges between the blood flow, the surrounding vessel tissue, and the flow-deviator.
2. Deep reinforcement learning algorithms to identify and invent new stent concepts, enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state.

This approach has never been done before in this context and should thus open both new theoretical and numerical opportunities.

Objectives of CURE

CURE takes the vital steps of bringing novel computational and optimization frameworks to the next level. The objectives include:

• Studying the selected flow diverter treatment to reduce the risk of hemorrhage in cerebral aneurysms.
• Supporting the decisions of treatment options by medical doctors.
• Providing guidance in the development of new implant designs.

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 outcomes.

Future Impact

The proposed work has the potential to reshape the future of intracranial aneurysm risk management. It is highly multidisciplinary, and the methods proposed and developed as part of this research can be quickly adapted to a wide range of engineering and biomedical applications.