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MXL

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Cerrado

Enhanced patient safety by computational Modelling from clinically available X r...

Enhanced patient safety by computational Modelling from clinically available X rays to minimise the risk of overload and instability for optimised function and joint Longevity Osteorthritis (OA) is a disabling disease, affecting the joints of 40% or more of the population over 60, resulting in a socioeconomic burden of €7b. per year in Germany alone. Joint surgery attempts to address the various disease... ver más

31/12/2012

SOUTHAMPTON

5M€

Presupuesto del proyecto: 5M€

Líder del proyecto

UNIVERSITY OF SOUTHAMPTON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 9 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-12-31 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

9 Participantes

SOUTHAMPTON

0.00€ | Lider

ZIB

N.D. | Participante

CHARITE UNIVERSITAETSMEDIZI...

N.D. | Participante

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Líder del proyecto

UNIVERSITY OF SOUTHAMPTON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 5M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Osteorthritis (OA) is a disabling disease, affecting the joints of 40% or more of the population over 60, resulting in a socioeconomic burden of €7b. per year in Germany alone. Joint surgery attempts to address the various disease stages, to either minimize the risk of early degeneration of the native joint, or to replace a fully degenerated joint, with well over 1,000,000 surgeries performed annually in the EU. Adequate joint function and longevity are the most important factors that define success, yet at least 10% of reconstructions fail within 10 years of surgery, and a large fraction of native joints progresses to early OA. The ensuing revision surgery is not only painful and stressful for the patient but also costly for the health care providers. Joint failure is a consequence of inadequate competence of the patient's musculoskeletal system, joint overload and instability, or their combination. Currently, surgeons rely on 2D static radiographs and their experience to plan the procedure. To prevent failures and improve outcome, key facts on the mechanical conditions of the joint need to be available to the surgeon. By implementing refined image reconstruction, biomechanical modelling and analysis tools, MXL will make the dynamic joint loading and stability accessible for the planning of joint surgery in every case. Based on this technology, MXL safeguards the patient by supporting the surgeon to decide when to operate, which procedure to use, and which key aspects to address to reduce the failure rate and improve the functional outcome of joint surgery. Therefore, we will develop an ICT based planning environment that provides the surgeon with quantitative information on the patient's anatomy, the competence of the soft and hard tissues, and integrates them within biomechanical models to arrive at an optimal strategy for joint surgery. This will result in a breakthrough with tremendous effects on patient safety and the return of function and joint longevity.

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