A Hybrid AI Framework for Cardiovascular Digital Twins: Integrating Data-Driven and Physics-Informed Models
2025 66th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2025): Proceedings
2025
Marta Narigina,
Andrejs Romānovs,
Jurijs Merkurjevs
In computational cardiology, a paradigm shift has
occurred with the transition from static cardiovascular risk assessment
to dynamic, customized modeling. A hybrid conceptual
framework for AI-based digital twins is presented in this paper,
which combines simulation models informed by physics and datadriven
perception models in a synergistic way. For conditions
like myocardial infarction and stroke, this strategy seeks to
provide previously unheard-of possibilities for disease prediction,
real-time cardiovascular monitoring, and customized treatment
optimization. Key elements of the framework include graph
neural networks (GNNs) for modeling vascular topology, physicsinformed
neural networks (PINNs) for hemodynamic analysis,
and multi-scale mathematical underpinnings. We illustrate a
crucial first step toward the realization of a comprehensive digital
twin that is based on physiological first principles and responsive
to real-
Keywords
Digital twins, cardiovascular modeling, artificial intelligence, physics-informed neural networks, myocardial infarction, stroke, hybrid models
DOI
10.1109/ITMS67030.2025.11236709
Hyperlink
https://ieeexplore.ieee.org/document/11236709
Narigina, M., Romānovs, A., Merkurjevs, J. A Hybrid AI Framework for Cardiovascular Digital Twins: Integrating Data-Driven and Physics-Informed Models. In: 2025 66th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2025): Proceedings, Latvia, Riga, 9-10 October, 2025. Piscataway: IEEE, 2025, pp.1-7. ISBN 979-8-3315-4529-1. e-ISBN 979-8-3315-4528-4. ISSN 2771-6953. e-ISSN 2771-6937. Available from: doi:10.1109/ITMS67030.2025.11236709
Publication language
English (en)