IIP
Individualized Implant Placement
| Coordinatore | GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 168˙224 € |
| EC contributo | 150˙000 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2013-PoC |
| Funding Scheme | CSA-SA(POC) |
| Anno di inizio | 2014 |
| Periodo (anno-mese-giorno) | 2014-01-01 - 2014-12-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
MEDIZINISCHE HOCHSCHULE HANNOVER
Organization address
address: Carl-Neuberg-Strasse 1 contact info |
DE (HANNOVER) | beneficiary | 42˙800.00 |
| 2 |
GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER
Organization address
address: Welfengarten 1 contact info |
DE (HANNOVER) | hostInstitution | 107˙200.00 |
Mappa
Word cloud
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Obiettivo del progetto (Objective)
'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.'