euHeart Project by CORDIS News

The EU's Seventh Framework Programme (FP7) has awarded EUR 14 million to a 4-year project, euHeart, for the improvement of the diagnosis, therapy and treatment of cardiovascular disease (CVD). The consortium comprises public and private partners from 16 research, academic, industrial and medical organisations from 6 European countries.

In the EU alone, CVD takes the lives of 1.9 million people annually and costs an estimated EUR 105 billion in healthcare. Advances in the management of coronary heart disease and chronic heart failure are, therefore, seen as crucial to reducing the human cost and financial burden of CVD.

The euHeart consortium focuses on developing technologies for the diagnosis and treatment of heart conditions such as heart failure, coronary artery disease, heart rhythm disorders and congenital heart defects. Specifically, it aims to develop computer models of the heart on multiple scales, from the molecular level to that of the whole organ, that can be adapted to individual patients.

The computer models will be functional as well as structural, incorporating clinical knowledge of how CVD affects the heart at each level. It is hoped that this will lead to the development of tools designed to predict outcomes for different therapies or treatments; if models can be personalised to individual patients, therapy and treatment could be equally personalised.

A person suffering from CVD could benefit from having a personalised computer model of their heart because it would address their own peculiarities. For example, the electrical activity in every patient's heart is subtly different; for certain conditions a computerised model reflecting the patient's unique heart structure and function would enable doctors to test the results of destroying different areas of tissue before they have to operate.

Multi-scale models have been used mainly in basic research, as the difficulty of adapting these models to individual human beings makes clinical applications impractical. To overcome this problem, the euHeart project intends to develop its models using novel information and communication technologies together with existing clinical data such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound scans, as well as measurements of blood flow and blood pressure in the coronary arteries and electrocardiograms. Gene defects in individual patients could also be taken into account.

Pre-diagnosed conditions such as heart arrhythmias would likely be the first to benefit from advances in computer modelling of CVD. Heart failure, coronary artery disease and diseases of the heart valves and aorta would also be major clinical focus areas.

As in many fields of research, one of the challenges of CVD modelling is integrating the vast amount of emerging and existing data; establishing CVD models on multiple levels could provide a consistent framework for such integration. The euHeart project will establish an open-source framework (using standardised mark-up languages such as CellML and FieldML) for both normal and pathological models that will integrate and interconnect existing and future models from myriad areas of biological research. It will additionally establish a shared library of innovative tools for biophysical simulations, model personalisation and automated image analysis.

Creating the highly personalised tools proposed by the consortium is no small feat: the euHeart consortium brings together an incredible amount of expertise and talent from across the EU to make this mammoth task possible. Different parts of the program are co-ordinated by Philips Research, King's College London and the University of Oxford; the consortium also includes participants in Germany, Spain, France and Belgium. The project is part of the Virtual Physiological Human (VPH) initiative, which aims to produce a unified computer model of the entire human body as a single complex system.

Related news articles:

More information on the euHeart project:
http://www.research.philips.com/newscenter/
backgrounders/080820-euheart.html

More information on the parallel HeartCycle project:
http://www.research.philips.com/technologies/
healthcare/homehc/heartcycle/heartcycle-gen.html

Copyright ©European Communities, 2008
Neither the Office for Official Publications of the European Communities, nor any person acting on its behalf, is responsible for the use, which might be made of the attached information. The attached information is drawn from the Community R&D Information Service (CORDIS). The CORDIS services are carried on the CORDIS Host in Luxembourg - http://cordis.europa.eu. Access to CORDIS is currently available free-of-charge.

Most Popular Now

ChatGPT can Produce Medical Record Notes…

The AI model ChatGPT can write administrative medical notes up to ten times faster than doctors without compromising quality. This is according to a new study conducted by researchers at...

Can Language Models Read the Genome? Thi…

The same class of artificial intelligence that made headlines coding software and passing the bar exam has learned to read a different kind of text - the genetic code. That code...

Bayer and Google Cloud to Accelerate Dev…

Bayer and Google Cloud announced a collaboration on the development of artificial intelligence (AI) solutions to support radiologists and ultimately better serve patients. As part of the collaboration, Bayer will...

Study Shows Human Medical Professionals …

When looking for medical information, people can use web search engines or large language models (LLMs) like ChatGPT-4 or Google Bard. However, these artificial intelligence (AI) tools have their limitations...

Shared Digital NHS Prescribing Record co…

Implementing a single shared digital prescribing record across the NHS in England could avoid nearly 1 million drug errors every year, stopping up to 16,000 fewer patients from being harmed...

North West Anglia Works with Clinisys to…

North West Anglia NHS Foundation Trust has replaced two, legacy laboratory information systems with a single instance of Clinisys WinPath. The trust, which serves a catchment of 800,000 patients in North...

Ask Chat GPT about Your Radiation Oncolo…

Cancer patients about to undergo radiation oncology treatment have lots of questions. Could ChatGPT be the best way to get answers? A new Northwestern Medicine study tested a specially designed ChatGPT...

Can AI Techniques Help Clinicians Assess…

Investigators have applied artificial intelligence (AI) techniques to gait analyses and medical records data to provide insights about individuals with leg fractures and aspects of their recovery. The study, published in...

AI Makes Retinal Imaging 100 Times Faste…

Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is...

SPARK TSL Acquires Sentean Group

SPARK TSL is acquiring Sentean Group, a Dutch company with a complementary background in hospital entertainment and communication, and bringing its Fusion Bedside platform for clinical and patient apps to...

Standing Up for Health Tech and SMEs: Sh…

AS the new chair of the health and social care council at techUK, Shane Tickell talked to Highland Marketing about his determination to support small and innovative companies, by having...

GPT-4 Matches Radiologists in Detecting …

Large language model GPT-4 matched the performance of radiologists in detecting errors in radiology reports, according to research published in Radiology, a journal of the Radiological Society of North America...