Development of Intelligent Digital Twins for Personalized Cardiovascular Disease Management

We would like to contribute to a project for the development of automated clinical decision support tools for vascular surgeons to quantitatively assess the influence of blood flow dynamics on thrombus formation. By integrating state-of-the-art computational fluid dynamics (CFD) modelling, advanced artificial intelligence methods, and automated medical image processing, the proposed solution will enable the creation of personalised patient models derived from multimodal imaging data. This approach aims to enhance the prediction and stratification of postoperative thrombogenic risk, thereby informing clinical decision-making and improving patient outcomes.