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Machine learning techniques for the optimization of joint replacements : application to a short-stem hip implant
Publikationstyp
Journal Article
Date Issued
2017-09-01
Sprache
English
Author(s)
Journal
Volume
12
Issue
9
Article Number
e0183755
Citation
PLoS ONE 12 (9): e0183755 (2017)
Publisher DOI
Scopus ID
Publisher
Public Library of Science
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.
DDC Class
610: Medicine, Health