Recent progress in the fields of computational modeling, data science, and artificial intelligence have shaken the landscape of cardiovascular research. In parallel, state-of-the-art medical imaging technologies now provide streams of patient data of unprecedented volume and detail. The convergence of these advances with the growth in processing power and communication infrastructure has made it possible to run patient-specific models of high fidelity or in large numbers, and empowered inter-disciplinary collaborations that are accelerating further developments of multi-scale, multi-physics models.
These synergies are driving a revolution in cardiovascular science by offering increasingly realistic digital twins to investigate disease mechanisms, facilitate medical device design and implantation planning, search for therapeutic targets, and improve diagnosis. However, the translation of computational advances to improve clinical decision support systems also faces significant challenges. It is necessary to be able to handle and manage vast amounts of data, coming from various sources and with different formats. Running patient-specific models with high fidelity requires significant computational resources. The development of multi-scale and multi-fidelity models requires significant integration efforts. Furthermore, the models need to represent the intricacies of the cardiovascular system by parameters that are often difficult to measure. Before clinical support systems can be implemented, rigorous validation against real-world clinical data is required. Other challenges are related to regulation, ethics or gaining the trust in the models of healthcare professionals, such as doctors and nurses, among others. Overcoming these challenges is essential to realize the full potential of current cardiovascular research and requires a new generation of scientists trained in all these aspects.
This course will review core competencies and novel developments in cardiac computational modeling. Particular attention will be given to multi-physics, multi-scale model development, digital twins and model-aided patient phenotyping, incorporating patient-specific data beyond vessel or chamber geometry, and simulation pipeline automation and integration into clinical decision support systems. The course curriculum will cover fundamental topics in cardiac biomechanics and state-of-the-art modeling techniques. It will also discuss machine learning and AI tools for image analysis and provide examples of computational models adopted into clinical decision support systems.
The course is aimed at graduate students of applied sciences and engineering interested in all aspects of cardiac bio-electro-mechanics, including imaging and computing. Researchers specializing in one field (e.g., electrophysiology) interested in branching out to other areas (e.g., fluid-structure interaction) are also welcome to attend this course. The course will feature poster sessions to spark cross-disciplinary communication among participants.