Systematic review of computational modelling for biomechanics analysis of total knee replacement. Issue 1 (27th February 2020)

• Record Type: Journal Article
• Authors: Shu, Liming; Li, Shihao; Sugita, Naohiko
• Is Part Of: Biosurface and biotribology. Volume 6:Issue 1(2020)
• Page Start: 3
• Page End: 11
• Publication Date: 2020-02-27
• Subjects: bone -- neural nets -- biomechanics -- orthopaedics -- prosthetics -- medical computing -- reviews -- design of experiments -- probability; systematic review -- computational modelling -- biomechanics analysis -- total knee replacement -- knee joint mechanics -- implant performance -- design stage -- boundary conditions -- computational experiments -- design‐of‐experiments -- subjective modelling -- preclinical testing -- in vivo testing -- in vitro testing -- material properties -- probabilistic methodology -- neural network methodology; Tribology -- Periodicals; Biomedical materials -- Periodicals; Tribology -- Congresses -- Periodicals; Biomedical materials -- Congresses -- Periodicals; Biological interfaces -- Congresses -- Periodicals; Biological interfaces; Biomedical materials; Tribology; Biocompatible Materials; Surface Properties; Materials Testing; Periodicals; Electronic journals; Periodical; Electronic journals; 610.28
• DOI: 10.1049/bsbt.2019.0012
• Languages: English
• ISSNs: 2405-4518

Abstract

Abstract : In vitro and in vivo testing can provide insight into knee joint mechanics and implant performance. However, these methods are costly and time‐consuming, which always limits their widespread use during the design stage of the implant. This review presents a critical analysis of computational modelling (in‐silicon) techniques including (i) development of a generic model of total knee replacement (TKR) and application of material properties, loading, and boundary conditions; (ii) design and execution of computational experiments; and (iii) practical applications and significant findings. The results show that the generic model and techniques provide significant insight into the general performance of TKR but have limited explicit validation. The introduction of design‐of‐experiments, probabilistic, and neural network methodologies in computational modelling has enabled simulation at the population level. Further advances in subjective modelling appear to be limited, mainly because of the lack of subjective materials and boundary conditions. Computational modelling will increasingly be used in the preclinical testing and design of TKR. This modelling should include subjective, multi‐scale, and closely corroborated analyses to account for the variability of TKR.