DISHEARTProject reference: 513226
Funded under: FP6-SME
Grid based decision support system for clinical diagnosis and Interventions in cardiovascular problems [Print to PDF] [Print to RTF]
Total cost:EUR 1 923 500
EU contribution:EUR 1 069 750
Call for proposal:FP6-2002-SME-1
Funding scheme:Cooperative - SMEs-Co-operative research contracts
The DISHEART project aims at developing a new computer based decision support system (DSS) integrating medical image data, modelling, simulation, computational Grid technologies and artificial intelligence methods for assisting clinical diagnosis and intervention in cardiovascular problems. The RTD goal is to improve and link existing state of the art technologies in order to build a computerised cardiovascular model for the analysis of the heart and blood vessels. The resulting DISHEART DSS will interface computational biomechanical analysis tools with the information coming from multimodal medical images. The computational model will be coupled to an artificial neural network (ANN) based decision model that can be educated for each particular patient with data coming from his/her images and/or analyses. The DISHEART DSS system will be validated in trials of clinical diagnosis, surgical intervention and subject-specific design of medical devices in the cardiovascular domain. The DISHEART DSS will also contribute to a better understanding of cardiovascular morphology and function as inferred from routine imaging examinations. Four reputable medical centers in Europe will take an active role in the validation and dissemination of the DISHEART DSS. The integrated DISHEART DSS will support health professionals in taking promptly the best possible decision for prevention, diagnosis and treatment. Emphasis will be put in the development of user-friendly, fast and reliable tools and interfaces providing access to heterogeneous health information sources, as well as on new methods for decision support and risk analysis. The use of Grid computing technology will be essential in order to optimise and distribute the heavy computational work required for physical modelling and numerical simulations and especially for the extensive parametric analysis required during the education of the DSS for every particular application.