193. Knowledge-driven and data-driven models of the thrombectomy procedure for acute ischemic stroke

Abstract:

Mechanical thrombectomy, a minimally invasive treatment for acute ischemic stroke, aims to remove a clot from a large cerebral vessel using stent retrievers and/or aspiration catheters. Finite element analyses, once properly validated, can be employed to investigate key aspects of this procedure. However, their high computational cost, in terms of both time and resources, prevents their direct integration into the clinical workflow. This limitation motivates the transition from knowledge-driven models to data-driven approaches.

About the author:

Virginia Fregona received the MSc degree in Biomedical Engineering cum laude in 2023 from Politecnico di Milano (Italy) with a thesis carried out in collaboration with the Erasmus Medical Center of Rotterdam (The Netherlands), where she spent three months as a visiting student. After graduating she enrolled in the PhD program in Bioengineering at Politecnico di Milano. She is carrying out her research activity in the In Silico Medicine group of the Laboratory of Biological Structure Mechanics (LaBS) at Politecnico di Milano and she is currently in the third and final yearof her PhD. She is also involved in the Horizon Europe project GEMINI, which aims to developmulti-organ and multi-scale digital twins in healthcare, supporting treatment selection for stroke.

researchgate.net/scientific-contributions/Virginia-Fregona