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Individualized Implant Placement

"The goal of the Proof of Concept (PoC) project is a licensing software (e.g. within a spin-off) that provides a framework to compute a suited (individualized) implant placement for patients: E.g. for hip or knee replacements, dur... ver más

31/12/2014

LUH

168K€

Presupuesto del proyecto: 168K€

Líder del proyecto

GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOV... 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 2 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-12-31 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.

2 Participantes

LUH

0.00€ | Lider

MHH

N.D. | Participante

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Líder del proyecto

GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOV... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 168K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "The goal of the Proof of Concept (PoC) project is a licensing software (e.g. within a spin-off) that provides a framework to compute a suited (individualized) implant placement for patients: E.g. for hip or knee replacements, during an operation planning phase, a surgeon selects an implant and determines its placement and possible motion range. Since humans are highly individual, factors such as gender, age, height or weight, among many other parameters influence the implant type and placement. Indeed the implant placement is always sub-optimal as the decision is solely based on the surgeons experience. A wrong placement can reduce the quality of life of the patient after the operation. Our software will close this gap and provide surgeons with the required key-information to make a high accurate individualized surgery planning: Our method will exploit statistical information about human shape and motion to extract this information. The software needs to be fast and accurate and must allow to take into account semantic meta information about the patient, e.g. by exploiting information such as gender, weight, height, age, etc. Based on our knowledge from the ERC starting Grant Dynamic MinVIP, this can be done in a statistical setting by training a PCA-model and by linear regression on the semantic parameters. Whereas our concept focuses on knee and hip treatment, it should be emphasized, that the concept is very general and has similar applications for e.g. hearing aids, aesthetic surgeries (e.g. breast cancer or burn patients), face/limb reconstruction or for the design of other orthopaedic devices, such as thrombosis stockings. We further expect insights which will help surgeons and engineers for the design of implants, which again will be of major importance for a company."

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