000063019 001__ 63019
000063019 005__ 20190709135512.0
000063019 0247_ $$2doi$$a10.1371/journal.pone.0183755
000063019 0248_ $$2sideral$$a101648
000063019 037__ $$aART-2017-101648
000063019 041__ $$aeng
000063019 100__ $$0(orcid)0000-0002-8503-9291$$aCilla, M.
000063019 245__ $$aMachine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant
000063019 260__ $$c2017
000063019 5060_ $$aAccess copy available to the general public$$fUnrestricted
000063019 5203_ $$aToday, different implant designs exist in the market; however, there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. Therefore, the aim of this project was to investigate if the geometry of a commercial short stem hip prosthesis can be further optimized to reduce stress shielding effects and achieve better short-stemmed implant performance. To reach this aim, the potential of machine learning techniques combined with parametric Finite Element analysis was used. The selected implant geometrical parameters were: total stem length (L), thickness in the lateral (R1) and medial (R2) and the distance between the implant neck and the central stem surface (D). The results show that the total stem length was not the only parameter playing a role in stress shielding. An optimized implant should aim for a decreased stem length and a reduced length of the surface in contact with the bone. The two radiuses that characterize the stem width at the distal cross-section in contact with the bone were less influential in the reduction of stress shielding compared with the other two parameters; but they also play a role where thinner stems present better results.
000063019 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000063019 590__ $$a2.766$$b2017
000063019 591__ $$aMULTIDISCIPLINARY SCIENCES$$b15 / 64 = 0.234$$c2017$$dQ1$$eT1
000063019 592__ $$a1.164$$b2017
000063019 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2017$$dQ1
000063019 593__ $$aMedicine (miscellaneous)$$c2017$$dQ1
000063019 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2017$$dQ1
000063019 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000063019 700__ $$aBorgiani, E.
000063019 700__ $$aMartínez, J.
000063019 700__ $$aDuda, G.N.
000063019 700__ $$aCheca, S.
000063019 773__ $$g12, 9 (2017), [16 pp]$$pPLoS One$$tPloS one$$x1932-6203
000063019 8564_ $$s4678636$$uhttps://zaguan.unizar.es/record/63019/files/texto_completo.pdf$$yVersión publicada
000063019 8564_ $$s104070$$uhttps://zaguan.unizar.es/record/63019/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000063019 909CO $$ooai:zaguan.unizar.es:63019$$particulos$$pdriver
000063019 951__ $$a2019-07-09-11:52:15
000063019 980__ $$aARTICLE