Digital Twins in Coronary Artery Disease: A Mathematical Roadmap

TL;DR

This paper proposes a mathematical framework for creating Digital Twins to improve diagnosis and treatment of Coronary Artery Disease, focusing on personalized Wall Shear Stress estimation.

Contribution

It introduces a mathematical roadmap integrating data assimilation and probabilistic models for Digital Twins in coronary artery disease management.

Findings

Outlines a bidirectional communication system for Digital Twins.

Emphasizes the importance of Wall Shear Stress in prognosis.

Provides steps for personalization and synthesis of stress estimation.

Abstract

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.