Digital Twins in Coronary Artery Disease: A Mathematical Roadmap
This is a conceptual framework for clinicians and researchers working on personalized medicine for coronary artery disease, but it lacks empirical validation and is therefore incremental.
The paper proposes a mathematical roadmap for constructing a Digital Twin system to prevent and treat Coronary Artery Disease, focusing on Wall Shear Stress estimation through Data Assimilation and Probabilistic Graphic Models. No concrete results or numbers are provided.
The combination of data and models, enhanced by AI methodologies, leads to the paradigm called Digital Twins. This concept is expected to bring unprecedented support to personalized medicine. The combination of mathematical and numerical models with diagnostic devices that provide patient-specific knowledge in a bidirectional framework can be a formidable decision support for clinicians. In this paper, we consider some mathematical aspects of constructing a Digital Twin to prevent and treat Coronary Artery Disease. The keywords for the bidirectional communication between twins in our system are (i) Data Assimilation and (ii) Probabilistic Graphic Models. In particular, a quantity of paramount interest in the evaluation and prognosis of Coronary Artery Disease is the Wall Shear Stress, i.e., the tangential component of normal stress on the arterial wall. By considering steps for the personalization and the synthesis of Wall Shear Stress estimation, we propose a mathematical roadmap for constructing a Digital Twin system that could help prevent infarcts, one of the most lethal diseases in the world.