euHeart

Cardiovascular disease (CVD) has a significant impact on the European society in terms of mortality, morbidity and allied healthcare costs. The opportunity of multi-scale modelling spanning, sub-cellular level up to whole heart is to improve CVD outcomes by providing a consistent, biophysically-based framework for the integration of the huge amount of fragmented and inhomogeneous data currently available. However, multi-scale models have not yet been translated into clinical environments mainly due to the difficulty of personalising biophysical models. The challenge of the euHeart project is to directly address this need by combining novel ICT technologies with integrative multi-scale computational models of the heart in clinical environments to improve diagnosis, treatment planning and interventions for CVD.

To meet this challenge we will bring together leading European physiological modelling and cardiac groups to develop, integrate and clinically validate patient-specific computational models of the cardiac physiology and disease-related processes. The main outcome of euHeart will be an open source framework for the description and representation of normal and pathological multi-scale and multi-physics cardiovascular models, using the international encoding standards. In addition, a library of innovative tools for the execution of the biophysical simulations, the personalisation of the models and the automated analysis of multi-modal images are developed.

Evidence of clinical benefit will be collected to quantify potential impact for a number of significant CVD's namely, heart failure, cardiac rhythm disorder, coronary artery disease and valvular and aortic diseases. Each of the selected clinical applications provides a complementary focus for the resulting integrated model of cardiac fluid-electro-mechanical function. The consortium contains a mix of academic leadership, clinical sites, and industrial partners ensuring exploitation of the wealth of models.

For further information, please visit:
http://www.euheart.org

Project co-ordinator:
Philips Technologie GmbH

Partners:

  • INRIA, Institut National de Recherche en Informatique et en Automatique
  • King's College London
  • Academisch Medisch Centrum bij de Universiteit van Amsterdam
  • Polydimensions GmbH
  • Universitat Pompeu Fabra
  • The University of Sheffield
  • Hospital Clinico San Carlos de Madrid Insalud
  • Philips Iberica S.A.
  • Institut National de la Santé et de la Recherche Médicale (INSERM)
  • Volcano Europe SA/NV
  • The Chancellor, Master and Scholars of the University of Oxford
  • HemoLab B.V.
  • Deutsche Krebsforschungszentrum (DKFZ)
  • Berlin Heart GmbH
  • Universität Karlsruhe (Technische Hochschule)
  • Philips Medical Systems Nederland BV

Timetable: from 06/2008 – to 05/2012

Total cost: € 19.053.465

EC funding: € 13.900.000

Programme Acronym: FP7-ICT

Subprogramme Area: Virtual physiological human

Contract type: Collaborative project (generic)


Related news article:

Most Popular Now

Open Medical Works with Moray's Dig…

Open Medical is working with the Digital Health & Care Innovation Centre’s Rural Centre of Excellence on a referral management plan, as part of a research and development scheme to...

Generative AI on Track to Shape the Futu…

Using advanced artificial intelligence (AI), researchers have developed a novel method to make drug development faster and more efficient. In a new paper, Xia Ning, lead author of the study and...

AI could Help Improve Early Detection of…

A new study led by investigators at the UCLA Health Jonsson Comprehensive Cancer Center suggests that artificial intelligence (AI) could help detect interval breast cancers - those that develop between...

Reorganisation, Consolidation, and Cuts:…

NHS England has been downsized and abolished. Integrated care boards have been told to change function, consolidate, and deliver savings. Trusts are planning big cuts. The Highland Marketing advisory board...

AI-Human Task-Sharing could Cut Mammogra…

The most effective way to harness the power of artificial intelligence (AI) when screening for breast cancer may be through collaboration with human radiologists - not by wholesale replacing them...

AI Tool Uses Face Photos to Estimate Bio…

Eyes may be the window to the soul, but a person's biological age could be reflected in their facial characteristics. Investigators from Mass General Brigham developed a deep learning algorithm...

Siemens Healthineers infection Control S…

Klinikum Region Hannover (KRH) has commissioned Siemens Healthineers to install infection control system (ICS) at the Klinikum Siloah hospital. The ICS aims to effectively tackle nosocomial infections and increase patient...

Philips Future Health Index 2025 Report …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today unveiled its 2025 Future Health Index U.S. report, "Building trust in healthcare AI," spotlighting the state of...

AI-Powered Precision: Unlocking the Futu…

A team of researchers from the Department of Diagnostic and Therapeutic Ultrasonography at the Tianjin Medical University Cancer Institute & Hospital, have published a review in Cancer Biology & Medicine...

AI Model Improves Delirium Prediction, L…

An artificial intelligence (AI) model improved outcomes in hospitalized patients by quadrupling the rate of detection and treatment of delirium. The model identifies patients at high risk for delirium and...

SALSA: A New AI Tool for the Automated a…

Investigators of the Vall d'Hebron Institute of Oncology's (VHIO) Radiomics Group, led by Raquel Perez-Lopez, have developed SALSA (System for Automatic Liver tumor Segmentation And detection), a fully automated deep...

Call for Papers: AI Applications in Biom…

JMIR Biomedical Engineering is inviting submissions for a new section titled "AI Applications in Biomedical Engineering." This themed section explores the integration of biomedical engineering and artificial intelligence (AI), focusing...