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Determining the anatomical site in knee radiographs using deep learning

An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior–posterior direction. In this retrospective study, a ResNet-34 network was trained on 2892 r...

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Autores principales: Quinsten, Anton S., Umutlu, Lale, Forsting, Michael, Nassenstein, Kai, Demircioğlu, Aydin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900105/
https://www.ncbi.nlm.nih.gov/pubmed/35256736
http://dx.doi.org/10.1038/s41598-022-08020-7
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author Quinsten, Anton S.
Umutlu, Lale
Forsting, Michael
Nassenstein, Kai
Demircioğlu, Aydin
author_facet Quinsten, Anton S.
Umutlu, Lale
Forsting, Michael
Nassenstein, Kai
Demircioğlu, Aydin
author_sort Quinsten, Anton S.
collection PubMed
description An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior–posterior direction. In this retrospective study, a ResNet-34 network was trained on 2892 radiographs from 2540 patients to predict the anatomical side of knees in radiographs. The network was evaluated in an internal validation cohort of 932 radiographs of 816 patients and in an external validation cohort of 490 radiographs from 462 patients. The network showed an accuracy of 99.8% and 99.9% on the internal and external validation cohort, respectively, which is comparable to the accuracy of radiographers. Anatomical side in radiographs of the knee in anterior–posterior direction can be deduced from radiographs with high accuracy using deep learning.
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spelling pubmed-89001052022-03-07 Determining the anatomical site in knee radiographs using deep learning Quinsten, Anton S. Umutlu, Lale Forsting, Michael Nassenstein, Kai Demircioğlu, Aydin Sci Rep Article An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior–posterior direction. In this retrospective study, a ResNet-34 network was trained on 2892 radiographs from 2540 patients to predict the anatomical side of knees in radiographs. The network was evaluated in an internal validation cohort of 932 radiographs of 816 patients and in an external validation cohort of 490 radiographs from 462 patients. The network showed an accuracy of 99.8% and 99.9% on the internal and external validation cohort, respectively, which is comparable to the accuracy of radiographers. Anatomical side in radiographs of the knee in anterior–posterior direction can be deduced from radiographs with high accuracy using deep learning. Nature Publishing Group UK 2022-03-07 /pmc/articles/PMC8900105/ /pubmed/35256736 http://dx.doi.org/10.1038/s41598-022-08020-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Quinsten, Anton S.
Umutlu, Lale
Forsting, Michael
Nassenstein, Kai
Demircioğlu, Aydin
Determining the anatomical site in knee radiographs using deep learning
title Determining the anatomical site in knee radiographs using deep learning
title_full Determining the anatomical site in knee radiographs using deep learning
title_fullStr Determining the anatomical site in knee radiographs using deep learning
title_full_unstemmed Determining the anatomical site in knee radiographs using deep learning
title_short Determining the anatomical site in knee radiographs using deep learning
title_sort determining the anatomical site in knee radiographs using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900105/
https://www.ncbi.nlm.nih.gov/pubmed/35256736
http://dx.doi.org/10.1038/s41598-022-08020-7
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