Systematic review of computational modelling for biomechanics analysis of total knee replacement
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 modelli...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-02-01
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| Series: | Biosurface and Biotribology |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/bsbt.2019.0012 |
| _version_ | 1818331162380075008 |
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| author | Liming Shu Shihao Li Shihao Li Naohiko Sugita |
| author_facet | Liming Shu Shihao Li Shihao Li Naohiko Sugita |
| author_sort | Liming Shu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T13:15:28Z |
| format | Article |
| id | doaj.art-a79d81cce6e34df2a3a94b38ec0ce512 |
| institution | Directory Open Access Journal |
| issn | 2405-4518 |
| language | English |
| last_indexed | 2024-12-13T13:15:28Z |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Biosurface and Biotribology |
| spelling | doaj.art-a79d81cce6e34df2a3a94b38ec0ce5122022-12-21T23:44:33ZengWileyBiosurface and Biotribology2405-45182020-02-0110.1049/bsbt.2019.0012BSBT.2019.0012Systematic review of computational modelling for biomechanics analysis of total knee replacementLiming Shu0Shihao Li1Shihao Li2Naohiko Sugita3School of Engineering, The University of TokyoSchool of Engineering, The University of TokyoSchool of Engineering, The University of TokyoSchool of Engineering, The University of TokyoIn 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.https://digital-library.theiet.org/content/journals/10.1049/bsbt.2019.0012boneneural netsbiomechanicsorthopaedicsprostheticsmedical computingreviewsdesign of experimentsprobabilitysystematic reviewcomputational modellingbiomechanics analysistotal knee replacementknee joint mechanicsimplant performancedesign stageboundary conditionscomputational experimentsdesign-of-experimentssubjective modellingpreclinical testingin vivo testingin vitro testingmaterial propertiesprobabilistic methodologyneural network methodology |
| spellingShingle | Liming Shu Shihao Li Shihao Li Naohiko Sugita Systematic review of computational modelling for biomechanics analysis of total knee replacement Biosurface and Biotribology 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 |
| title | Systematic review of computational modelling for biomechanics analysis of total knee replacement |
| title_full | Systematic review of computational modelling for biomechanics analysis of total knee replacement |
| title_fullStr | Systematic review of computational modelling for biomechanics analysis of total knee replacement |
| title_full_unstemmed | Systematic review of computational modelling for biomechanics analysis of total knee replacement |
| title_short | Systematic review of computational modelling for biomechanics analysis of total knee replacement |
| title_sort | systematic review of computational modelling for biomechanics analysis of total knee replacement |
| topic | 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 |
| url | https://digital-library.theiet.org/content/journals/10.1049/bsbt.2019.0012 |
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