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

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

RSNA AI Challenge Models can Independent…

Algorithms submitted for an AI Challenge hosted by the Radiological Society of North America (RSNA) have shown excellent performance for detecting breast cancers on mammography images, increasing screening sensitivity while...

AI could Help Emergency Rooms Predict Ad…

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the...

Head-to-Head Against AI, Pharmacy Studen…

Students pursuing a Doctor of Pharmacy degree routinely take - and pass - rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new...

NHS Active 10 Walking Tracker Users are …

Users of the NHS Active 10 app, designed to encourage people to become more active, immediately increased their amount of brisk and non-brisk walking upon using the app, according to...

New AI Tool Illuminates "Dark Side…

Proteins sustain life as we know it, serving many important structural and functional roles throughout the body. But these large molecules have cast a long shadow over a smaller subclass...

Deep Learning-Based Model Enables Fast a…

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for definitive diagnosis. However, conventional...