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

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Project objective

Cardiovascular disease (CVD) remains one of the leading global health burdens, with mortality projected to rise from 18.9 million deaths in 2020 to more than 32 million by 2050. Research highlights that effective CVD management depends not only on medical interventions but also on active patient involvement in decision-making. In this context, digital health technologies—particularly decision support systems (DSS) and virtual twins (VT)—offer promising solutions to enhance personalised care, empower patients and clinicians, and reduce healthcare costs.Virtual twins are 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 virtual twins, AI-driven data analytics, and visualisation technologies to advance personalised CVD management. By merging expertise across disciplines, REMERGE seeks to overcome current fragmentation, accelerate innovation, and promote the integration of digital twin technologies into real-world clinical workflows—ultimately

Original text from CORDIS.

Participants

• CHAROKOPEIO PANEPISTIMIO · AthinaCoordinatorGreece
• DEXTERA CONSULTING LTD · NicosiaCyprus
• DIAGNOSTIKON KAI THERAPEFTIKON KENTRON ATHINON YGEIA ANONYMOS ETAIREIA · MAROUSIGreece
• SISTEM GLOBAL DANISMANLIK ANONIM SIRKETI · ISTANBULTürkiye
• TECHNISCHE UNIVERSITEIT DELFT · DelftNetherlands
• UAB METIS BALTIC · VilniusLithuania
• UNIVERZA V MARIBORU · MariborSlovenia

Links

• View on CORDIS
• DOI: 10.3030/101300089