Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of dee...

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Autores principales:	Eun-Gyeong Kim, Il-Seok Oh, Jeong-Eun So, Junhyeok Kang, Van Nhat Thang Le, Min-Kyung Tak, Dae-Woo Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bone maturation cervical vertebrae maturation deep learning lateral cephalogram Medicine R
Acceso en línea:	https://doaj.org/article/e3cfdb716ae74a2ba8d6e0bad5ae5bd6
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id	oai:doaj.org-article:e3cfdb716ae74a2ba8d6e0bad5ae5bd6
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spelling	oai:doaj.org-article:e3cfdb716ae74a2ba8d6e0bad5ae5bd62021-11-25T18:02:26ZEstimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network10.3390/jcm102254002077-0383https://doaj.org/article/e3cfdb716ae74a2ba8d6e0bad5ae5bd62021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/22/5400https://doaj.org/toc/2077-0383Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.Eun-Gyeong KimIl-Seok OhJeong-Eun SoJunhyeok KangVan Nhat Thang LeMin-Kyung TakDae-Woo LeeMDPI AGarticlebone maturationcervical vertebrae maturationdeep learninglateral cephalogramMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5400, p 5400 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bone maturation cervical vertebrae maturation deep learning lateral cephalogram Medicine R
spellingShingle	bone maturation cervical vertebrae maturation deep learning lateral cephalogram Medicine R Eun-Gyeong Kim Il-Seok Oh Jeong-Eun So Junhyeok Kang Van Nhat Thang Le Min-Kyung Tak Dae-Woo Lee Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
description	Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.
format	article
author	Eun-Gyeong Kim Il-Seok Oh Jeong-Eun So Junhyeok Kang Van Nhat Thang Le Min-Kyung Tak Dae-Woo Lee
author_facet	Eun-Gyeong Kim Il-Seok Oh Jeong-Eun So Junhyeok Kang Van Nhat Thang Le Min-Kyung Tak Dae-Woo Lee
author_sort	Eun-Gyeong Kim
title	Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
title_short	Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
title_full	Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
title_fullStr	Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
title_full_unstemmed	Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network
title_sort	estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e3cfdb716ae74a2ba8d6e0bad5ae5bd6
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Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

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