Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep...

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Autores principales:	Chiaki Kuwada, Yoshiko Ariji, Yoshitaka Kise, Takuma Funakoshi, Motoki Fukuda, Tsutomu Kuwada, Kenichi Gotoh, Eiichiro Ariji
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a493db1ced3f4929b493e7338b127a10
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spelling	oai:doaj.org-article:a493db1ced3f4929b493e7338b127a102021-12-02T16:35:46ZDetection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system10.1038/s41598-021-95653-92045-2322https://doaj.org/article/a493db1ced3f4929b493e7338b127a102021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95653-9https://doaj.org/toc/2045-2322Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.Chiaki KuwadaYoshiko ArijiYoshitaka KiseTakuma FunakoshiMotoki FukudaTsutomu KuwadaKenichi GotohEiichiro ArijiNature 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 Chiaki Kuwada Yoshiko Ariji Yoshitaka Kise Takuma Funakoshi Motoki Fukuda Tsutomu Kuwada Kenichi Gotoh Eiichiro Ariji Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
description	Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.
format	article
author	Chiaki Kuwada Yoshiko Ariji Yoshitaka Kise Takuma Funakoshi Motoki Fukuda Tsutomu Kuwada Kenichi Gotoh Eiichiro Ariji
author_facet	Chiaki Kuwada Yoshiko Ariji Yoshitaka Kise Takuma Funakoshi Motoki Fukuda Tsutomu Kuwada Kenichi Gotoh Eiichiro Ariji
author_sort	Chiaki Kuwada
title	Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
title_short	Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
title_full	Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
title_fullStr	Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
title_full_unstemmed	Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
title_sort	detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a493db1ced3f4929b493e7338b127a10
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Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

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