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

Most Advanced Artificial Touch for Brain…

For the first time ever, a complex sense of touch for individuals living with spinal cord injuries is a step closer to reality. A new study published in Science, paves...

Predicting the Progression of Autoimmune…

Autoimmune diseases, where the immune system mistakenly attacks the body's own healthy cells and tissues, often have a preclinical stage before diagnosis that’s characterized by mild symptoms or certain antibodies...

Major EU Project to Investigate Societal…

A new €3 million EU research project led by University College Dublin (UCD) Centre for Digital Policy will explore the benefits and risks of Artificial Intelligence (AI) from a societal...

Using AI to Uncover Hospital Patients�…

Across the United States, no hospital is the same. Equipment, staffing, technical capabilities, and patient populations can all differ. So, while the profiles developed for people with common conditions may...

New AI Tool Uses Routine Blood Tests to …

Doctors around the world may soon have access to a new tool that could better predict whether individual cancer patients will benefit from immune checkpoint inhibitors - a type of...

New Method Tracks the 'Learning Cur…

Introducing Annotatability - a powerful new framework to address a major challenge in biological research by examining how artificial neural networks learn to label genomic data. Genomic datasets often contain...

Picking the Right Doctor? AI could Help

Years ago, as she sat in waiting rooms, Maytal Saar-Tsechansky began to wonder how people chose a good doctor when they had no way of knowing a doctor's track record...

From Text to Structured Information Secu…

Artificial intelligence (AI) and above all large language models (LLMs), which also form the basis for ChatGPT, are increasingly in demand in hospitals. However, patient data must always be protected...

AI Innovation Unlocks Non-Surgical Way t…

Researchers have developed an artificial intelligence (AI) model to detect the spread of metastatic brain cancer using MRI scans, offering insights into patients’ cancer without aggressive surgery. The proof-of-concept study, co-led...

Deep Learning Model Helps Detect Lung Tu…

A new deep learning model shows promise in detecting and segmenting lung tumors, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA)...

New Study Reveals AI's Transformati…

Intensive care units (ICUs) face mounting pressure to effectively manage resources while delivering optimal patient care. Groundbreaking research published in the INFORMS journal Information Systems Research highlights how a novel...

One of the Largest Global Surveys of Soc…

As leaders gather for the World Economic Forum Annual Meeting 2025 in Davos, Leaps by Bayer, the impact investing arm of Bayer, and Boston Consulting Group (BCG) announced the launch...