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

dc.creatorDiego A. Orozco Villase�or
dc.creatorMarkus A. Wimmer
dc.date2016
dc.date.accessioned2018-10-18T15:34:04Z
dc.date.available2018-10-18T15:34:04Z
dc.identifier.issn23146133
dc.identifier.doi10.1155/2016/2071945
dc.identifier.urihttp://hdl.handle.net/11285/630256
dc.descriptionThe 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. © 2016 Diego A. Orozco Villaseñor and Markus A. Wimmer.
dc.languageeng
dc.publisherHindawi Limited
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84984691282&doi=10.1155%2f2016%2f2071945&partnerID=40&md5=ba44a7dc931e384ae47fb9995060c900
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceBioMed Research International
dc.subjectpolyethylene
dc.subjectpolyethylene
dc.subjectArticle
dc.subjectartificial neural network
dc.subjectcontrolled study
dc.subjectdata mining
dc.subjectfemale
dc.subjecthuman
dc.subjectinterrater reliability
dc.subjectmale
dc.subjectscar
dc.subjectsimulator
dc.subjecttotal knee arthroplasty
dc.subjectartificial neural network
dc.subjectcomputer simulation
dc.subjectgait
dc.subjectknee replacement
dc.subjectpathophysiology
dc.subjectphysiology
dc.subjectprostheses and orthoses
dc.subjectrehabilitation
dc.subjectscar
dc.subjectArthroplasty, Replacement, Knee
dc.subjectCicatrix
dc.subjectComputer Simulation
dc.subjectGait
dc.subjectHumans
dc.subjectNeural Networks (Computer)
dc.subjectPolyethylene
dc.subjectProstheses and Implants
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleWear Scar Similarities between Retrieved and Simulator-Tested Polyethylene TKR Components: An Artificial Neural Network Approach
dc.typeArtículo
dc.identifier.volume2016
refterms.dateFOA2018-10-18T15:34:04Z


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