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A convolutional neural network for high throughput screening of femoral stem taper corrosion
Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. Thi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350735/ https://www.ncbi.nlm.nih.gov/pubmed/37300244 http://dx.doi.org/10.1177/09544119231177834 |
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author | Codirenzi, Anastasia M Lanting, Brent A Teeter, Matthew G |
author_facet | Codirenzi, Anastasia M Lanting, Brent A Teeter, Matthew G |
author_sort | Codirenzi, Anastasia M |
collection | PubMed |
description | Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. This limits the quantity of implants retrieval studies typically analyze. Machine learning, particularly convolutional neural networks, have been used in various medical imaging applications and corrosion detection applications to help reduce repetitive and tedious image identification tasks. 725 retrieved modular femoral stem arthroplasty devices had their trunnion imaged in four positions and scored by an observer. A convolutional neural network was designed and trained from scratch using the images. There were four classes, each representing one of the established Goldberg corrosion classes. The composition of the classes were as follows: class 1 (n = 1228), class 2 (n = 1225), class 3 (n = 335), and class 4 (n = 102). The convolutional neural network utilized a single convolutional layer and RGB coloring. The convolutional neural network was able to distinguish no and mild corrosion (classes 1 and 2) from moderate and severe corrosion (classes 3 and 4) with an accuracy of 98.32%, a class 1 and 2 sensitivity of 0.9881, a class 3 and 4 sensitivity of 0.9556 and an area under the curve of 0.9740. This convolutional neural network may be used as a screening tool to identify retrieved modular hip arthroplasty device trunnions for further study and the presence of moderate and severe corrosion with high reliability, reducing the burden on skilled observers. |
format | Online Article Text |
id | pubmed-10350735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-103507352023-07-18 A convolutional neural network for high throughput screening of femoral stem taper corrosion Codirenzi, Anastasia M Lanting, Brent A Teeter, Matthew G Proc Inst Mech Eng H Original Articles Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. This limits the quantity of implants retrieval studies typically analyze. Machine learning, particularly convolutional neural networks, have been used in various medical imaging applications and corrosion detection applications to help reduce repetitive and tedious image identification tasks. 725 retrieved modular femoral stem arthroplasty devices had their trunnion imaged in four positions and scored by an observer. A convolutional neural network was designed and trained from scratch using the images. There were four classes, each representing one of the established Goldberg corrosion classes. The composition of the classes were as follows: class 1 (n = 1228), class 2 (n = 1225), class 3 (n = 335), and class 4 (n = 102). The convolutional neural network utilized a single convolutional layer and RGB coloring. The convolutional neural network was able to distinguish no and mild corrosion (classes 1 and 2) from moderate and severe corrosion (classes 3 and 4) with an accuracy of 98.32%, a class 1 and 2 sensitivity of 0.9881, a class 3 and 4 sensitivity of 0.9556 and an area under the curve of 0.9740. This convolutional neural network may be used as a screening tool to identify retrieved modular hip arthroplasty device trunnions for further study and the presence of moderate and severe corrosion with high reliability, reducing the burden on skilled observers. SAGE Publications 2023-06-09 2023-07 /pmc/articles/PMC10350735/ /pubmed/37300244 http://dx.doi.org/10.1177/09544119231177834 Text en © IMechE 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Articles Codirenzi, Anastasia M Lanting, Brent A Teeter, Matthew G A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title | A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title_full | A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title_fullStr | A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title_full_unstemmed | A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title_short | A convolutional neural network for high throughput screening of femoral stem taper corrosion |
title_sort | convolutional neural network for high throughput screening of femoral stem taper corrosion |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350735/ https://www.ncbi.nlm.nih.gov/pubmed/37300244 http://dx.doi.org/10.1177/09544119231177834 |
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