Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network

Abstract Cervical ossification of the posterior longitudinal ligament (OPLL) is a contributing factor to spinal cord injury or trauma-induced myelopathy in the elderly. To reduce the incidence of these traumas, it is essential to diagnose OPLL at an early stage and to educate patients how to prevent...

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Autores principales:	Masataka Miura, Satoshi Maki, Kousei Miura, Hiroshi Takahashi, Masayuki Miyagi, Gen Inoue, Kazuma Murata, Takamitsu Konishi, Takeo Furuya, Masao Koda, Masashi Takaso, Kenji Endo, Seiji Ohtori, Masashi Yamazaki
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/54239e1f5c6a4de59a2ad4931808d2df
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spelling	oai:doaj.org-article:54239e1f5c6a4de59a2ad4931808d2df2021-12-02T16:04:22ZAutomated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network10.1038/s41598-021-92160-92045-2322https://doaj.org/article/54239e1f5c6a4de59a2ad4931808d2df2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92160-9https://doaj.org/toc/2045-2322Abstract Cervical ossification of the posterior longitudinal ligament (OPLL) is a contributing factor to spinal cord injury or trauma-induced myelopathy in the elderly. To reduce the incidence of these traumas, it is essential to diagnose OPLL at an early stage and to educate patients how to prevent falls. We thus evaluated the ability of our convolutional neural network (CNN) to differentially diagnose cervical spondylosis and cervical OPLL. We enrolled 250 patients with cervical spondylosis, 250 patients with cervical OPLL, and 180 radiographically normal controls. We evaluated the ability of our CNN model to distinguish cervical spondylosis, cervical OPLL, and controls, and the diagnostic accuracy was compared to that of 5 board-certified spine surgeons. The accuracy, average recall, precision, and F1 score of the CNN for classification of lateral cervical spine radiographs were 0.86, 0.86, 0.87, and 0.87, respectively. The accuracy was higher for CNN compared to any expert spine surgeon, and was statistically equal to 4 of the 5 experts and significantly higher than that of 1 expert. We demonstrated that the performance of the CNN was equal or superior to that of spine surgeons.Masataka MiuraSatoshi MakiKousei MiuraHiroshi TakahashiMasayuki MiyagiGen InoueKazuma MurataTakamitsu KonishiTakeo FuruyaMasao KodaMasashi TakasoKenji EndoSeiji OhtoriMasashi YamazakiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masataka Miura Satoshi Maki Kousei Miura Hiroshi Takahashi Masayuki Miyagi Gen Inoue Kazuma Murata Takamitsu Konishi Takeo Furuya Masao Koda Masashi Takaso Kenji Endo Seiji Ohtori Masashi Yamazaki Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
description	Abstract Cervical ossification of the posterior longitudinal ligament (OPLL) is a contributing factor to spinal cord injury or trauma-induced myelopathy in the elderly. To reduce the incidence of these traumas, it is essential to diagnose OPLL at an early stage and to educate patients how to prevent falls. We thus evaluated the ability of our convolutional neural network (CNN) to differentially diagnose cervical spondylosis and cervical OPLL. We enrolled 250 patients with cervical spondylosis, 250 patients with cervical OPLL, and 180 radiographically normal controls. We evaluated the ability of our CNN model to distinguish cervical spondylosis, cervical OPLL, and controls, and the diagnostic accuracy was compared to that of 5 board-certified spine surgeons. The accuracy, average recall, precision, and F1 score of the CNN for classification of lateral cervical spine radiographs were 0.86, 0.86, 0.87, and 0.87, respectively. The accuracy was higher for CNN compared to any expert spine surgeon, and was statistically equal to 4 of the 5 experts and significantly higher than that of 1 expert. We demonstrated that the performance of the CNN was equal or superior to that of spine surgeons.
format	article
author	Masataka Miura Satoshi Maki Kousei Miura Hiroshi Takahashi Masayuki Miyagi Gen Inoue Kazuma Murata Takamitsu Konishi Takeo Furuya Masao Koda Masashi Takaso Kenji Endo Seiji Ohtori Masashi Yamazaki
author_facet	Masataka Miura Satoshi Maki Kousei Miura Hiroshi Takahashi Masayuki Miyagi Gen Inoue Kazuma Murata Takamitsu Konishi Takeo Furuya Masao Koda Masashi Takaso Kenji Endo Seiji Ohtori Masashi Yamazaki
author_sort	Masataka Miura
title	Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
title_short	Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
title_full	Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
title_fullStr	Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
title_full_unstemmed	Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
title_sort	automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/54239e1f5c6a4de59a2ad4931808d2df
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Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network

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