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Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs
Deep learning is an advanced machine learning technique that is used in several medical fields to diagnose diseases and predict therapeutic outcomes. In this study, using anteroposterior ankle radiographs, we developed a convolutional neural network (CNN) model to diagnose osteochondral lesions of t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174357/ https://www.ncbi.nlm.nih.gov/pubmed/37171314 http://dx.doi.org/10.1097/MD.0000000000033796 |
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author | Shin, Hyunkwang Park, Donghwi Kim, Jeoung Kun Choi, Gyu Sang Chang, Min Cheol |
author_facet | Shin, Hyunkwang Park, Donghwi Kim, Jeoung Kun Choi, Gyu Sang Chang, Min Cheol |
author_sort | Shin, Hyunkwang |
collection | PubMed |
description | Deep learning is an advanced machine learning technique that is used in several medical fields to diagnose diseases and predict therapeutic outcomes. In this study, using anteroposterior ankle radiographs, we developed a convolutional neural network (CNN) model to diagnose osteochondral lesions of the talus (OLTs) using ankle radiographs as input data. We evaluated whether a CNN model trained on anteroposterior ankle radiographs could help diagnose the presence of OLT. We retrospectively collected 379 cases (OLT cases = 133, non-OLT cases = 246) of anteroposterior ankle radiographs taken at a university hospital between January 2010 and December 2020. The OLT was diagnosed using ankle magnetic resonance images of each patient. Among the 379 cases, 70% of the included data were randomly selected as the training set, 10% as the validation set, and the remaining 20% were assigned to the test set to evaluate the model performance. To accurately classify OLT and non-OLT, we cropped the area of the ankle on anteroposterior ankle radiographs, resized the image to 224 × 224, and used it as the input data. We then used the Visual Geometry Group Network model to determine whether the input image was OLT or non-OLT. The performance of the CNN model for the area under the curve, accuracy, positive predictive value, and negative predictive value on the test data were 0.774 (95% confidence interval [CI], 0.673–0.875), 81.58% (95% CI, 0.729–0.903), 80.95% (95% CI, 0.773–0.846), and 81.82% (95% CI, 0.804–0.832), respectively. A CNN model trained on anteroposterior ankle radiographs achieved meaningful accuracy in diagnosing OLT and demonstrated that it could help diagnose OLT. |
format | Online Article Text |
id | pubmed-10174357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-101743572023-05-12 Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs Shin, Hyunkwang Park, Donghwi Kim, Jeoung Kun Choi, Gyu Sang Chang, Min Cheol Medicine (Baltimore) 6300 Deep learning is an advanced machine learning technique that is used in several medical fields to diagnose diseases and predict therapeutic outcomes. In this study, using anteroposterior ankle radiographs, we developed a convolutional neural network (CNN) model to diagnose osteochondral lesions of the talus (OLTs) using ankle radiographs as input data. We evaluated whether a CNN model trained on anteroposterior ankle radiographs could help diagnose the presence of OLT. We retrospectively collected 379 cases (OLT cases = 133, non-OLT cases = 246) of anteroposterior ankle radiographs taken at a university hospital between January 2010 and December 2020. The OLT was diagnosed using ankle magnetic resonance images of each patient. Among the 379 cases, 70% of the included data were randomly selected as the training set, 10% as the validation set, and the remaining 20% were assigned to the test set to evaluate the model performance. To accurately classify OLT and non-OLT, we cropped the area of the ankle on anteroposterior ankle radiographs, resized the image to 224 × 224, and used it as the input data. We then used the Visual Geometry Group Network model to determine whether the input image was OLT or non-OLT. The performance of the CNN model for the area under the curve, accuracy, positive predictive value, and negative predictive value on the test data were 0.774 (95% confidence interval [CI], 0.673–0.875), 81.58% (95% CI, 0.729–0.903), 80.95% (95% CI, 0.773–0.846), and 81.82% (95% CI, 0.804–0.832), respectively. A CNN model trained on anteroposterior ankle radiographs achieved meaningful accuracy in diagnosing OLT and demonstrated that it could help diagnose OLT. Lippincott Williams & Wilkins 2023-05-12 /pmc/articles/PMC10174357/ /pubmed/37171314 http://dx.doi.org/10.1097/MD.0000000000033796 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 6300 Shin, Hyunkwang Park, Donghwi Kim, Jeoung Kun Choi, Gyu Sang Chang, Min Cheol Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title | Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title_full | Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title_fullStr | Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title_full_unstemmed | Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title_short | Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
title_sort | development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs |
topic | 6300 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174357/ https://www.ncbi.nlm.nih.gov/pubmed/37171314 http://dx.doi.org/10.1097/MD.0000000000033796 |
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