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Health

GridSystems’ solutions provide the answer to the growing demand for large processing power to handle the increasing amount of digital data in the health industry. Computer based drug design, medical imaging, and medical simulations are some of the areas in need of a more technologically advanced solution. GridSystems’ solutions help create an environment where information ranging from molecule analysis to population-wide studies can be associated in order to provide individualized healthcare.

Some examples of medical processes that imply a growing need for grid solutions are: analysis of 2D, 3D images, clinical trials, medical simulations, etc. For example, physicians can plug in data involving patients with high blood pressure and diabetes and, then, run various scenarios using grid distributed computing. This approach rapidly determines the benefits or disadvantages of alternative prescriptions. Another example refers to medical imaging diagnostics through grid computation. It allows a cardiologist, for example, to compare her patient’s heart with thousands of heart models, enabling the doctor to characterize the heart of the patient, make a better diagnose, check the effectiveness of a treatment, and even use real-time simulations in the course of a surgical procedure. Grid solutions provide health-care companies and medical institutions the possibility to improve their research and production processes and to offer more effective services to their customers.

  Example: Hospital Clínico Barcelona    
 

A cluster of research groups at the University Pompeu Fabra works in image analysis and computer vision in the area of facial biometry and computational analysis of cardiovascular medical images for the Hospital Clínico de Barcelona.

 
 

With Fura EE, the calculations are distributed across the idle computers in the network of the University, critically reducing the amount of time needed to obtain results, from months to hours, thus making feasible real time diagnostic of this and many other medical procedures. In cardiovascular imaging this methodology will be soon used to analyze the patient’s data in real time, which would improve the survival rates during critical episodes.