GMV innovation hailed by of the European Commission’s Innovation Radar: Innoradar

The European Commission’s innovation radar has recognized GMV as a “Key Innovator” on the strength of its developments in natural deformation and 3D handling of medical images, helping surgeons to prepare and plan their work.

GMV’s inhouse research has come up trumps, proving capable of the highly complex achievement of modeling complete relations between anatomical structures and their elastic performance and developing algorithms capable of capturing these complexities, dealing with medical images in near real time.

Carlos Illana, Product Manager of GMV’s Secure e-Solutions sector, gives the following account: «Many different aspects of GMV’s inhouse R&D under the Rainbow project have been weighed up by the European Commission, hailing in particular the deformation simulation algorithms and volumetric medical image segmentation. This helps to solve one of the main challenges of pre-surgery planning, which has hitherto been restricted to bone structures». As Illana points out, «this groundbreaking innovation would not have been possible without the joint work of highly specialized engineers with trailblazing business expertise too and also worldwide public research bodies. The Horizon 2020 Innovative Training Networks (ITN) represent «a great chance to drive innovation forward, promote highly skilled European personnel training and encourage knowledge transfer between public research bodies and private enterprise».

The European Commission’s Innoradar has been set up to identify high potential innovations and innovators in EU-funded research and innovation projects. Its remit is to bring projects to the notice of potential investors, driving the marketing of these developments and the creation of an innovating ecosystem. It is also part of the overarching transparency exercise, to ensure the public at large is kept informed of how EU funds are spent.

GMV’s Key Innovator status has been awarded by independent experts that audit all programs developed under the various European programs: Horizon 2020, Framework Programme 7 (FP7) or the Competitiveness and Innovation Framework Programme (CIP). The parameters assessed for this award include the level of maturity of the products and services developed for marketing and of the developing organizations themselves, valuing aspects such as disruptiveness and degree of development of the technology and methodology employed and the input and impact for society as a whole. In this particular case the nomination stemmed from GMV’s work under the RAINBOW project.

On RAINBOW

The goal of the RAINBOW project (Rapid biomechanical simulation for personalized clinical design) is to perform research and development into a new generation of practical, user-friendly clinical simulators that are conducive to the design and application of personalized medicine. Under RAINBOW GMV is helping to develop clinical simulation tools to be applied in procedures of diagnosis, prognosis, monitoring, surgical training, planning, guidance, prosthesis design, implant operations and medical devices. Clinicians will then be able to use this new generation of biomechanical simulation tools directly without needing technical help.

RAINBOW is included in the Innovative Training Networks (ITN) of the Horizon 2020 program. GMV is collaborating in the project with universities and research centers of worldwide prestige such as the Universidad Rey Juan Carlos, Cardiff University in Wales, France’s National Scientific Research Center, Luxembourg University, Hvidovre hospital and Aalborg and Kobenhavns universities in Germany.

Clinicians have many different design-based decision-making responsibilities, whether designing treatment or planning surgical operations, etc. ICT tools such as GMV’s inhouse developments under RAINBOW help to set up virtual physiological models and apply personalized computational medicine. These breakthroughs enable specialist clinicians to make decisions based on precise information and work with patient simulation models, boosting their results and performance considerably.

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