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Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network
Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501139/ https://www.ncbi.nlm.nih.gov/pubmed/36143096 http://dx.doi.org/10.3390/jcm11185450 |
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author | Trinh, Giam Minh Shao, Hao-Chiang Hsieh, Kevin Li-Chun Lee, Ching-Yu Liu, Hsiao-Wei Lai, Chen-Wei Chou, Sen-Yi Tsai, Pei-I Chen, Kuan-Jen Chang, Fang-Chieh Wu, Meng-Huang Huang, Tsung-Jen |
author_facet | Trinh, Giam Minh Shao, Hao-Chiang Hsieh, Kevin Li-Chun Lee, Ching-Yu Liu, Hsiao-Wei Lai, Chen-Wei Chou, Sen-Yi Tsai, Pei-I Chen, Kuan-Jen Chang, Fang-Chieh Wu, Meng-Huang Huang, Tsung-Jen |
author_sort | Trinh, Giam Minh |
collection | PubMed |
description | Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis. |
format | Online Article Text |
id | pubmed-9501139 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95011392022-09-24 Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network Trinh, Giam Minh Shao, Hao-Chiang Hsieh, Kevin Li-Chun Lee, Ching-Yu Liu, Hsiao-Wei Lai, Chen-Wei Chou, Sen-Yi Tsai, Pei-I Chen, Kuan-Jen Chang, Fang-Chieh Wu, Meng-Huang Huang, Tsung-Jen J Clin Med Article Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis. MDPI 2022-09-16 /pmc/articles/PMC9501139/ /pubmed/36143096 http://dx.doi.org/10.3390/jcm11185450 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Trinh, Giam Minh Shao, Hao-Chiang Hsieh, Kevin Li-Chun Lee, Ching-Yu Liu, Hsiao-Wei Lai, Chen-Wei Chou, Sen-Yi Tsai, Pei-I Chen, Kuan-Jen Chang, Fang-Chieh Wu, Meng-Huang Huang, Tsung-Jen Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title | Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title_full | Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title_fullStr | Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title_full_unstemmed | Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title_short | Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network |
title_sort | detection of lumbar spondylolisthesis from x-ray images using deep learning network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501139/ https://www.ncbi.nlm.nih.gov/pubmed/36143096 http://dx.doi.org/10.3390/jcm11185450 |
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