Classification of caries in third molars on panoramic radiographs using deep learning

Abstract The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious...

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Autores principales:	Shankeeth Vinayahalingam, Steven Kempers, Lorenzo Limon, Dionne Deibel, Thomas Maal, Marcel Hanisch, Stefaan Bergé, Tong Xi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/caeb7548de31431a93e364bae1311e86
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spelling	oai:doaj.org-article:caeb7548de31431a93e364bae1311e862021-12-02T17:24:00ZClassification of caries in third molars on panoramic radiographs using deep learning10.1038/s41598-021-92121-22045-2322https://doaj.org/article/caeb7548de31431a93e364bae1311e862021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92121-2https://doaj.org/toc/2045-2322Abstract The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.Shankeeth VinayahalingamSteven KempersLorenzo LimonDionne DeibelThomas MaalMarcel HanischStefaan BergéTong XiNature 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 Shankeeth Vinayahalingam Steven Kempers Lorenzo Limon Dionne Deibel Thomas Maal Marcel Hanisch Stefaan Bergé Tong Xi Classification of caries in third molars on panoramic radiographs using deep learning
description	Abstract The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.
format	article
author	Shankeeth Vinayahalingam Steven Kempers Lorenzo Limon Dionne Deibel Thomas Maal Marcel Hanisch Stefaan Bergé Tong Xi
author_facet	Shankeeth Vinayahalingam Steven Kempers Lorenzo Limon Dionne Deibel Thomas Maal Marcel Hanisch Stefaan Bergé Tong Xi
author_sort	Shankeeth Vinayahalingam
title	Classification of caries in third molars on panoramic radiographs using deep learning
title_short	Classification of caries in third molars on panoramic radiographs using deep learning
title_full	Classification of caries in third molars on panoramic radiographs using deep learning
title_fullStr	Classification of caries in third molars on panoramic radiographs using deep learning
title_full_unstemmed	Classification of caries in third molars on panoramic radiographs using deep learning
title_sort	classification of caries in third molars on panoramic radiographs using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/caeb7548de31431a93e364bae1311e86
work_keys_str_mv	AT shankeethvinayahalingam classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT stevenkempers classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT lorenzolimon classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT dionnedeibel classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT thomasmaal classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT marcelhanisch classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT stefaanberge classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning AT tongxi classificationofcariesinthirdmolarsonpanoramicradiographsusingdeeplearning
_version_	1718380994976808960

Classification of caries in third molars on panoramic radiographs using deep learning

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