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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-8900105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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