At HealthGrid 2009 the Health-e-Child project opened the plenery session though having won the competitions for best presentation and poster in 2008 the consortium did not enter either competition. Images The Opening of the Plenary Session
Highlights of the presentation of the Health-e-Child platform by David Manset of GKnowledge and Joerg Freund of Siemens were recorded and uploaded to YouTube. And can also be found through this post on the GridCast blog.
The first part of the presentation simulates a clinician retrieving anonymised patient data from the Grid along with a 4D model of the right ventricle and utilising this information to a perform similarity search within the patient population using neighbourhood graphs. The goal for the project is for this process to be able to support clinical decision making processes, such as assessing when to proceed with a surgical intervention.
The second highlight focuses on CardioViz. CardioViz is the tool that clinicians are using to make the 4D personalised heart modes which were mentioned in the first exert. The models that are produced are fully biometrically, atomically and electrically personalised and can be used to simulate a theorised surgical intervention such as the repair of a tear in the right ventricle. The tool will also be able to provide the clinician with a theoretical model of the blood flow through the ventral following the procedure, a tool that is especially useful for patients with Tetralogy of Fallot (ToF).
The final segment describes the 3D knowledge browser which can be used by clinical researchers or medical students to find more information about a patient’s ToF. After the user specifies the layers they are interested in the 3D bowledge browser will use these ontologies to find information from different data sources. The first is through PubMed which will find related papers, the second is through the Health-e-Child patient data and the third is through a bridge to the @neurIST Project. The link to the project allows Health-e-Child users to focus on the gene which pay be playing a role in the disease but utilising the @neurLink tool to find papers and information which correspond to the potentially interesting genes.
