Wear Scar Similarities between Retrieved and Simulator-Tested Polyethylene TKR Components: An Artificial Neural Network Approach

Abstract

The aim of this study was to determine how representative wear scars of simulator-tested polyethylene (PE) inserts compare with retrieved PE inserts from total knee replacement (TKR). By means of a nonparametric self-organizing feature map (SOFM), wear scar images of 21 postmortem- and 54 revision-retrieved components were compared with six simulator-tested components that were tested either in displacement or in load control according to ISO protocols. The SOFM network was then trained with the wear scar images of postmortem-retrieved components since those are considered well-functioning at the time of retrieval. Based on this training process, eleven clusters were established, suggesting considerable variability among wear scars despite an uncomplicated loading history inside their hosts. The remaining components (revision-retrieved and simulator-tested) were then assigned to these established clusters. Six out of five simulator components were clustered together, suggesting that the network was able to identify similarities in loading history. However, the simulator-tested components ended up in a cluster at the fringe of the map containing only 10.8% of retrieved components. This may suggest that current ISO testing protocols were not fully representative of this TKR population, and protocols that better resemble patients' gait after TKR containing activities other than walking may be warranted.

A Comparison of Wear Patterns on Retrieved and Simulator-Tested Total Knee Replacements

“…However, the mean lifetime of the analyzed retrievals is comparable with the mean running times of the analyzed inserts from the simulator tests, according to Battenberg et al [ 31 ]. Studies on retrievals with average lifetimes comparable with those used in the present study are reported in the literature [ 20 , 21 , 23 , 25 ].…”

“…For total hip replacement, it is stated that standard wear simulator tests cannot predict the in vivo situation in an exact manner for the entire time of the implantation [ 11 , 17 , 18 ]. Additionally, for total knee replacement (TKR), some studies of retrievals and standard wear testing showed a significant discrepancy in wear data [ 19 , 20 , 21 , 22 , 23 ]. Previous studies had focused on implant malpositioning, overloading, and reasons for enhanced wear of TKR [ 21 , 24 , 25 ].…”

“…Previous studies had focused on implant malpositioning, overloading, and reasons for enhanced wear of TKR [ 21 , 24 , 25 ]. Some studies aimed to identify, detect, and quantify specific wear patterns of TKR [ 7 , 25 ], but the researchers used different implant designs to compare retrievals with implants from wear simulator studies or different computational approaches or compared the wear without examining the specific wear pattern [ 19 , 20 , 21 , 23 , 26 ]. Harman et al and Paltanea et al [ 21 , 27 ] showed the potential of the use of optical microscopy techniques in the examination of failed implants.…”

Are Instrumented Knee Forces Representative of a Larger Population of Cruciate-Retaining Total Knee Arthroplasties?

“…Although studies have investigated the difference in wear due to prosthesis design, it is unknown how using inputs for wear testing from populations of TKR patients implanted with different prostheses will affect the outcome of wear tests. However since wear tested simulator components continue to not be representative of wear measured on retrieved TKRs [35,36], work should continue towards defining accurate wear simulation inputs generalizable to multiple TKR patient cohorts who participate in a variety of daily activities.…”

Prediction of Polyethylene Wear Rates from Gait Biomechanics and Implant Positioning in Total Hip Replacement

“…The cross-validation procedure was repeated until each patient was used as the test subject in his or her corresponding wear group. ANN can be considered a nonlinear regression model that can model the relationship between the independent variables (predictors) and dependent variable in an analogous fashion to linear regression [4][5][6][7]38]. The most-featured difference between linear regression and ANN is that the latter splits a complex nonlinear relationship into several piecewise approximations, where each approximation is generated as a nonlinear function of predictors (instead of a simple linear combination of predictors as in linear regression).…”