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Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle
BACKGROUND: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evalu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403355/ https://www.ncbi.nlm.nih.gov/pubmed/34454471 http://dx.doi.org/10.1186/s12880-021-00660-x |
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author | Yan, Wen Meng, Xianghong Sun, Jinglai Yu, Hui Wang, Zhi |
author_facet | Yan, Wen Meng, Xianghong Sun, Jinglai Yu, Hui Wang, Zhi |
author_sort | Yan, Wen |
collection | PubMed |
description | BACKGROUND: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. METHODS: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. RESULTS: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. CONCLUSION: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury. |
format | Online Article Text |
id | pubmed-8403355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84033552021-08-30 Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle Yan, Wen Meng, Xianghong Sun, Jinglai Yu, Hui Wang, Zhi BMC Med Imaging Research BACKGROUND: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. METHODS: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. RESULTS: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. CONCLUSION: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury. BioMed Central 2021-08-28 /pmc/articles/PMC8403355/ /pubmed/34454471 http://dx.doi.org/10.1186/s12880-021-00660-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yan, Wen Meng, Xianghong Sun, Jinglai Yu, Hui Wang, Zhi Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title | Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title_full | Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title_fullStr | Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title_full_unstemmed | Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title_short | Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
title_sort | intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403355/ https://www.ncbi.nlm.nih.gov/pubmed/34454471 http://dx.doi.org/10.1186/s12880-021-00660-x |
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