Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Abstract This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was d...

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Autores principales:	Eun-Gyu Ha, Kug Jin Jeon, Young Hyun Kim, Jae-Young Kim, Sang-Sun Han
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cdeb3f4646034e828793dc6c80919e23
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spelling	oai:doaj.org-article:cdeb3f4646034e828793dc6c80919e232021-12-05T12:15:44ZAutomatic detection of mesiodens on panoramic radiographs using artificial intelligence10.1038/s41598-021-02571-x2045-2322https://doaj.org/article/cdeb3f4646034e828793dc6c80919e232021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02571-xhttps://doaj.org/toc/2045-2322Abstract This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.Eun-Gyu HaKug Jin JeonYoung Hyun KimJae-Young KimSang-Sun HanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eun-Gyu Ha Kug Jin Jeon Young Hyun Kim Jae-Young Kim Sang-Sun Han Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
description	Abstract This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.
format	article
author	Eun-Gyu Ha Kug Jin Jeon Young Hyun Kim Jae-Young Kim Sang-Sun Han
author_facet	Eun-Gyu Ha Kug Jin Jeon Young Hyun Kim Jae-Young Kim Sang-Sun Han
author_sort	Eun-Gyu Ha
title	Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
title_short	Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
title_full	Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
title_fullStr	Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
title_full_unstemmed	Automatic detection of mesiodens on panoramic radiographs using artificial intelligence
title_sort	automatic detection of mesiodens on panoramic radiographs using artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cdeb3f4646034e828793dc6c80919e23
work_keys_str_mv	AT eungyuha automaticdetectionofmesiodensonpanoramicradiographsusingartificialintelligence AT kugjinjeon automaticdetectionofmesiodensonpanoramicradiographsusingartificialintelligence AT younghyunkim automaticdetectionofmesiodensonpanoramicradiographsusingartificialintelligence AT jaeyoungkim automaticdetectionofmesiodensonpanoramicradiographsusingartificialintelligence AT sangsunhan automaticdetectionofmesiodensonpanoramicradiographsusingartificialintelligence
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Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

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