Cardiovascular Digital Twins from Physics Based to Data Driven Approaches
2026-08-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explain that cardiovascular digital twins are computer models that mimic a patient's heart and blood vessels to help doctors diagnose and treat heart problems. They discuss two main types: one that uses detailed physics to understand how the heart works but is slow to run, and another that uses lots of data to work faster but might be less reliable. They describe new methods that combine both approaches to get accurate and efficient models. The paper also looks at how to use these models with real patient data and the challenges in making them ready for everyday healthcare.
cardiovascular digital twinsmechanistic modelsdata-driven modelsphysics-informed modellinggraph-based learningdata assimilationvascular networkscomputational modellingclinical validationtherapy optimisation
Authors
Emmanuel Lwele, Francis Chikweto
Abstract
Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation. Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches improve scalability yet risk limited robustness. Emerging physics-informed, graph-based, and hybrid methods integrate physical constraints with relational learning across vascular networks. We review modelling paradigms, data assimilation frameworks, validation challenges, and translational pathways toward clinically deployable cardiovascular digital twins.