Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant.

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 paramet

SUBMITTER: Cilla M 

PROVIDER: S-EPMC5584793 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature