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Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network
The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior...
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/PMC8871012/ https://www.ncbi.nlm.nih.gov/pubmed/35204487 http://dx.doi.org/10.3390/diagnostics12020396 |
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author | Caesarendra, Wahyu Rahmaniar, Wahyu Mathew, John Thien, Ady |
author_facet | Caesarendra, Wahyu Rahmaniar, Wahyu Mathew, John Thien, Ady |
author_sort | Caesarendra, Wahyu |
collection | PubMed |
description | The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior-posterior X-ray image dataset and local images were used to train and test the proposed convolutional neural network architecture. Sixty-eight landmark features of the spine were detected from the input image to obtain seventeen vertebrae on the spine. The vertebrae locations obtained were processed to automatically measure the Cobb angle. The proposed method can measure the Cobb angle with accuracies up to 93.6% and has excellent reliability compared to clinicians’ measurement (intraclass correlation coefficient > 0.95). The proposed deep learning architecture may be used as a tool to augment Cobb angle measurement in X-ray images of patients with adolescent idiopathic scoliosis in a real-world clinical setting. |
format | Online Article Text |
id | pubmed-8871012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88710122022-02-25 Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network Caesarendra, Wahyu Rahmaniar, Wahyu Mathew, John Thien, Ady Diagnostics (Basel) Article The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior-posterior X-ray image dataset and local images were used to train and test the proposed convolutional neural network architecture. Sixty-eight landmark features of the spine were detected from the input image to obtain seventeen vertebrae on the spine. The vertebrae locations obtained were processed to automatically measure the Cobb angle. The proposed method can measure the Cobb angle with accuracies up to 93.6% and has excellent reliability compared to clinicians’ measurement (intraclass correlation coefficient > 0.95). The proposed deep learning architecture may be used as a tool to augment Cobb angle measurement in X-ray images of patients with adolescent idiopathic scoliosis in a real-world clinical setting. MDPI 2022-02-03 /pmc/articles/PMC8871012/ /pubmed/35204487 http://dx.doi.org/10.3390/diagnostics12020396 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 Caesarendra, Wahyu Rahmaniar, Wahyu Mathew, John Thien, Ady Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title | Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title_full | Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title_fullStr | Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title_full_unstemmed | Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title_short | Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network |
title_sort | automated cobb angle measurement for adolescent idiopathic scoliosis using convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871012/ https://www.ncbi.nlm.nih.gov/pubmed/35204487 http://dx.doi.org/10.3390/diagnostics12020396 |
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