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Research Empowerment and Knowledge Transfer for Personalized CVD Management based on VT/AI Technologies

CORDIS · observation · Publication date unknown

emerging tools that create digital replicas of patients, enabling simulation and prediction of disease progression and treatment outcomes. They rely on a combination of mechanistic, statistical, and machine learning models. Each modelling approach has unique advantages and limitations: mechanistic models provide explanatory power based on known physiological principles, while statistical and AI-driven models offer predictive capabilities using large datasets. Integrating these approaches can yield more accurate and comprehensive virtual representations. Although virtual twin applications in cardiology—such as virtual valve replacement, ablation guidance, and carotid stenosis detection—show encouraging results, most remain at the proof-of-concept or model validation stage, with limited clinical integration. Current research mainly focuses on organ-level modelling using imaging and biomechanical data, but significant gaps persist in clinical validation and multi-organ modelling. The REMERGE initiative aims to address these challenges by fostering collaboration among experts in virtu

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recordType

award

status

SIGNED

region

EU

value

1457910

unit

EUR