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

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

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2025-SE-01

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Coordinator

CHAROKOPEIO PANEPISTIMIO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 360 720,00
Address
ELEFTHERIOU VENIZELOU 70
176 71 Athina
Greece

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Region
Αττική Aττική Νότιος Τομέας Αθηνών
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Participants (6)