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A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These me...

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Detalles Bibliográficos
Autores principales: Jin, Chen, Wang, Shengru, Yang, Guodong, Li, En, Liang, Zize
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101880/
https://www.ncbi.nlm.nih.gov/pubmed/35590951
http://dx.doi.org/10.3390/s22093258
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author Jin, Chen
Wang, Shengru
Yang, Guodong
Li, En
Liang, Zize
author_facet Jin, Chen
Wang, Shengru
Yang, Guodong
Li, En
Liang, Zize
author_sort Jin, Chen
collection PubMed
description Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.
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spelling pubmed-91018802022-05-14 A Review of the Methods on Cobb Angle Measurements for Spinal Curvature Jin, Chen Wang, Shengru Yang, Guodong Li, En Liang, Zize Sensors (Basel) Review Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed. MDPI 2022-04-24 /pmc/articles/PMC9101880/ /pubmed/35590951 http://dx.doi.org/10.3390/s22093258 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 Review
Jin, Chen
Wang, Shengru
Yang, Guodong
Li, En
Liang, Zize
A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title_full A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title_fullStr A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title_full_unstemmed A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title_short A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
title_sort review of the methods on cobb angle measurements for spinal curvature
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101880/
https://www.ncbi.nlm.nih.gov/pubmed/35590951
http://dx.doi.org/10.3390/s22093258
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