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An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric...

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Autores principales: Kandel, Sushil, Su, Steven, Hall, Richard M., Tipper, Joanne L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285289/
https://www.ncbi.nlm.nih.gov/pubmed/37362220
http://dx.doi.org/10.3389/fbioe.2023.1108021
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author Kandel, Sushil
Su, Steven
Hall, Richard M.
Tipper, Joanne L.
author_facet Kandel, Sushil
Su, Steven
Hall, Richard M.
Tipper, Joanne L.
author_sort Kandel, Sushil
collection PubMed
description Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.
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spelling pubmed-102852892023-06-23 An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network Kandel, Sushil Su, Steven Hall, Richard M. Tipper, Joanne L. Front Bioeng Biotechnol Bioengineering and Biotechnology Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates. Frontiers Media S.A. 2023-06-08 /pmc/articles/PMC10285289/ /pubmed/37362220 http://dx.doi.org/10.3389/fbioe.2023.1108021 Text en Copyright © 2023 Kandel, Su, Hall and Tipper. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Kandel, Sushil
Su, Steven
Hall, Richard M.
Tipper, Joanne L.
An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title_full An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title_fullStr An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title_full_unstemmed An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title_short An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
title_sort automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285289/
https://www.ncbi.nlm.nih.gov/pubmed/37362220
http://dx.doi.org/10.3389/fbioe.2023.1108021
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