ABSTRACT
ABSTRACT Joint arthroplasty is a common treatment for arthritis, involving the surgical implantation of prostheses to replace the bearing surfaces of diseased joints. While the clinical success of joint replacement is high, up to 25% of total knee replacement (TKR) patients remain dissatised with the outcome of their procedure. There is a recognized need for premarketing assessment tools capable of predicting performance outcomes of joint prostheses, in order to shorten development times, avoid disastrous prosthesis failures that threaten patient safety, and optimize design of components that are robust to patient
CONTENTS
2.1 Signicance ........................................................................................................................... 10 2.2 Modeling Approaches ......................................................................................................... 11 2.3 Acquiring Subject-Specic Metrics Suitable for Model Input ....................................... 13 2.4 Development, Validation, and Application of a Computational Knee Simulator ...... 14
2.4.1 In Vitro Testing in an Experimental Knee Simulator ......................................... 15 2.4.2 Development of a Computational Counterpart to the Experimental
Simulator ................................................................................................................... 16 2.4.3 Validation of the Computational Model with Experimental Data ................... 17 2.4.4 Application of Computational Models to Clinical Issues .................................. 18 2.4.5 Summary ................................................................................................................... 21
2.5 Toward Simulations Which Reproduce Conditions during ADLs ...............................22 2.5.1 Incorporating Feedback Control of Joint Loads in Computational
Simulations ...............................................................................................................23 2.5.2 Enhanced Simulations to Better Reproduce ADLs ............................................. 24 2.5.3 Developing External Loading Conditions to Match Physiological Joint
Loads .......................................................................................................................... 26 2.5.4 Summary ................................................................................................................... 27
2.6 Incorporating Variability into Computational Models .................................................. 29 2.6.1 Development of an Efcient FE Model .................................................................30 2.6.2 Sources of Variability .............................................................................................. 31 2.6.3 Integrating Probabilistic Methods with FE Simulations .................................... 32 2.6.4 Summary ...................................................................................................................34
