Computational modelling of physiology has a long successful history as a unique tool to understand the very complex biological function from cellular to tissue and organ levels. The cardiovascular system has been a very active area in which computational modelling has played vital roles, from fundamental research to clinical patient management. With the development of computer technology, the fusion of computational modelling and artificial intelligence (AI) has been at the frontier of computational modelling of the cardiovascular system. The objective of this research topic is to spotlight the involvement of AI in the process of computational methods in the cardiovascular system, from modelling, and numerical methods to data processing.
The promising role of computational methods in cardiovascular system research has been proven in recent decades. In recent years, numerous sophisticated numerical methods coupled with the patient-specific model have been developed, which benefit the further revelation of the underlying mechanisms and facilitate more precise clinical management of cardiovascular diseases. However, the increasing complexity of the numerical modelling techniques induced difficulties in model preparation and required higher computational costs, which restricted their applications instead. The latest development of AI has shown its ability to solve complex physiological problems, which provided a viable way of overcoming the drawbacks of deploying computational methods in the cardiovascular system. The fusion of traditional computational methods and AI, termed AI-accelerated computation methods, has been another cutting-edge area in cardiovascular system research.
We welcome reviews and original articles describing new mathematical models, in-depth theoretical analysis, reduced-modelling, cutting-edge computational methods, novel statistical approaches, and machine-learning methods for analyzing and modelling the cardiovascular system. We welcome submissions related to but not limited to the following research fields:
• Computational modelling and analysis of the cardiac system from the cell, the tissue structure to the whole heart function, i.e., myocardium, valves and arterial wall, etc.;
• Hemodynamic modelling and analysis in large and small vessels, including systemic circulation, coronary circulation, atherosclerosis, etc.;
• Electrophysiology modelling and analysis from cell to tissue and the whole heart, such as action potential propagation, calcium dynamics, and electromechanical coupling applied to the diagnosis or treatment of cardiac diseases;
• Personalized modelling development: clinical data assimilation, parameter estimation, uncertainty quantification, etc.;
• AI-accelerated computation methods.
Computational modelling of physiology has a long successful history as a unique tool to understand the very complex biological function from cellular to tissue and organ levels. The cardiovascular system has been a very active area in which computational modelling has played vital roles, from fundamental research to clinical patient management. With the development of computer technology, the fusion of computational modelling and artificial intelligence (AI) has been at the frontier of computational modelling of the cardiovascular system. The objective of this research topic is to spotlight the involvement of AI in the process of computational methods in the cardiovascular system, from modelling, and numerical methods to data processing.
The promising role of computational methods in cardiovascular system research has been proven in recent decades. In recent years, numerous sophisticated numerical methods coupled with the patient-specific model have been developed, which benefit the further revelation of the underlying mechanisms and facilitate more precise clinical management of cardiovascular diseases. However, the increasing complexity of the numerical modelling techniques induced difficulties in model preparation and required higher computational costs, which restricted their applications instead. The latest development of AI has shown its ability to solve complex physiological problems, which provided a viable way of overcoming the drawbacks of deploying computational methods in the cardiovascular system. The fusion of traditional computational methods and AI, termed AI-accelerated computation methods, has been another cutting-edge area in cardiovascular system research.
We welcome reviews and original articles describing new mathematical models, in-depth theoretical analysis, reduced-modelling, cutting-edge computational methods, novel statistical approaches, and machine-learning methods for analyzing and modelling the cardiovascular system. We welcome submissions related to but not limited to the following research fields:
• Computational modelling and analysis of the cardiac system from the cell, the tissue structure to the whole heart function, i.e., myocardium, valves and arterial wall, etc.;
• Hemodynamic modelling and analysis in large and small vessels, including systemic circulation, coronary circulation, atherosclerosis, etc.;
• Electrophysiology modelling and analysis from cell to tissue and the whole heart, such as action potential propagation, calcium dynamics, and electromechanical coupling applied to the diagnosis or treatment of cardiac diseases;
• Personalized modelling development: clinical data assimilation, parameter estimation, uncertainty quantification, etc.;
• AI-accelerated computation methods.
