dc.contributor.author
Cilla, Myriam
dc.contributor.author
Borgiani, Edoardo
dc.contributor.author
Martinez, Javier
dc.contributor.author
Duda, Georg N.
dc.contributor.author
Checa, Sara
dc.date.accessioned
2018-06-08T10:40:31Z
dc.date.available
2017-10-20T10:32:18.101Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/20867
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-24166
dc.description.abstract
Today, different implant designs exist in the market; however, there is not a
clear understanding of which are the best implant design parameters to achieve
mechanical optimal conditions. Therefore, the aim of this project was to
investigate if the geometry of a commercial short stem hip prosthesis can be
further optimized to reduce stress shielding effects and achieve better short-
stemmed implant performance. To reach this aim, the potential of machine
learning techniques combined with parametric Finite Element analysis was used.
The selected implant geometrical parameters were: total stem length (L),
thickness in the lateral (R1) and medial (R2) and the distance between the
implant neck and the central stem surface (D). The results show that the total
stem length was not the only parameter playing a role in stress shielding. An
optimized implant should aim for a decreased stem length and a reduced length
of the surface in contact with the bone. The two radiuses that characterize
the stem width at the distal cross-section in contact with the bone were less
influential in the reduction of stress shielding compared with the other two
parameters; but they also play a role where thinner stems present better
results.
en
dc.format.extent
16 Seiten
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject.ddc
000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::006 Spezielle Computerverfahren
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::680 Industrielle Fertigung für einzelne Verwendungszwecke::680 Industrielle Fertigung für einzelne Verwendungszwecke
dc.title
Machine learning techniques for the optimization of joint replacements
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
PLoS ONE. - 12 (2017), 9, e0183755
dc.title.subtitle
Application to a short-stem hip implant
dcterms.bibliographicCitation.doi
10.1371/journal.pone.0183755
dcterms.bibliographicCitation.url
http://doi.org/10.1371/journal.pone.0183755
refubium.affiliation
Charité - Universitätsmedizin Berlin
de
refubium.mycore.fudocsId
FUDOCS_document_000000028356
refubium.note.author
Der Artikel wurde in einer Open-Access-Zeitschrift publiziert.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000009024
dcterms.accessRights.openaire
open access
dcterms.isPartOf.issn
1932-6203