A deep learning algorithm for automated measurement of vertebral body compression from X-ray images

Abstract The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evalua...

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Autores principales:	Jae Won Seo, Sang Heon Lim, Jin Gyo Jeong, Young Jae Kim, Kwang Gi Kim, Ji Young Jeon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d167dd4198354cd4bc44b44931e5e79c
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spelling	oai:doaj.org-article:d167dd4198354cd4bc44b44931e5e79c2021-12-02T16:10:37ZA deep learning algorithm for automated measurement of vertebral body compression from X-ray images10.1038/s41598-021-93017-x2045-2322https://doaj.org/article/d167dd4198354cd4bc44b44931e5e79c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93017-xhttps://doaj.org/toc/2045-2322Abstract The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-Net model, with an average sensitivity of 0.934 (± 0.086), an average specificity of 0.997 (± 0.002), an average accuracy of 0.987 (± 0.005), and an average dice similarity coefficient of 0.923 (± 0.073). We then generated 1134 data points on the images of three vertebral bodies by labeling each segment of the segmented vertebral body. These were used in the vertebral compression measurement model based on linear regression and multi-scale residual dilated blocks. The model yielded an average mean absolute error of 2.637 (± 1.872) (%), an average mean square error of 13.985 (± 24.107) (%), and an average root mean square error of 3.739 (± 2.187) (%) in fractured vertebral body data. The proposed algorithm has significant potential for aiding the diagnosis of vertebral compression fractures.Jae Won SeoSang Heon LimJin Gyo JeongYoung Jae KimKwang Gi KimJi Young JeonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jae Won Seo Sang Heon Lim Jin Gyo Jeong Young Jae Kim Kwang Gi Kim Ji Young Jeon A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
description	Abstract The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-Net model, with an average sensitivity of 0.934 (± 0.086), an average specificity of 0.997 (± 0.002), an average accuracy of 0.987 (± 0.005), and an average dice similarity coefficient of 0.923 (± 0.073). We then generated 1134 data points on the images of three vertebral bodies by labeling each segment of the segmented vertebral body. These were used in the vertebral compression measurement model based on linear regression and multi-scale residual dilated blocks. The model yielded an average mean absolute error of 2.637 (± 1.872) (%), an average mean square error of 13.985 (± 24.107) (%), and an average root mean square error of 3.739 (± 2.187) (%) in fractured vertebral body data. The proposed algorithm has significant potential for aiding the diagnosis of vertebral compression fractures.
format	article
author	Jae Won Seo Sang Heon Lim Jin Gyo Jeong Young Jae Kim Kwang Gi Kim Ji Young Jeon
author_facet	Jae Won Seo Sang Heon Lim Jin Gyo Jeong Young Jae Kim Kwang Gi Kim Ji Young Jeon
author_sort	Jae Won Seo
title	A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
title_short	A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
title_full	A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
title_fullStr	A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
title_full_unstemmed	A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
title_sort	deep learning algorithm for automated measurement of vertebral body compression from x-ray images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d167dd4198354cd4bc44b44931e5e79c
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A deep learning algorithm for automated measurement of vertebral body compression from X-ray images

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