Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is there...

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Autores principales:	Chun-Wei Li, Szu-Yin Lin, He-Sheng Chou, Tsung-Yi Chen, Yu-An Chen, Sheng-Yu Liu, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	biomedical image periapical image apical lesion Gaussian high pass filter iterative thresholding deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/dd261695f058410fb798223364d76d80
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spelling	oai:doaj.org-article:dd261695f058410fb798223364d76d802021-11-11T19:04:35ZDetection of Dental Apical Lesions Using CNNs on Periapical Radiograph10.3390/s212170491424-8220https://doaj.org/article/dd261695f058410fb798223364d76d802021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7049https://doaj.org/toc/1424-8220Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.Chun-Wei LiSzu-Yin LinHe-Sheng ChouTsung-Yi ChenYu-An ChenSheng-Yu LiuYu-Lin LiuChiung-An ChenYen-Cheng HuangShih-Lun ChenYi-Cheng MaoPatricia Angela R. AbuWei-Yuan ChiangWen-Shen LoMDPI AGarticlebiomedical imageperiapical imageapical lesionGaussian high pass filteriterative thresholdingdeep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7049, p 7049 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomedical image periapical image apical lesion Gaussian high pass filter iterative thresholding deep learning Chemical technology TP1-1185
spellingShingle	biomedical image periapical image apical lesion Gaussian high pass filter iterative thresholding deep learning Chemical technology TP1-1185 Chun-Wei Li Szu-Yin Lin He-Sheng Chou Tsung-Yi Chen Yu-An Chen Sheng-Yu Liu Yu-Lin Liu Chiung-An Chen Yen-Cheng Huang Shih-Lun Chen Yi-Cheng Mao Patricia Angela R. Abu Wei-Yuan Chiang Wen-Shen Lo Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
description	Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.
format	article
author	Chun-Wei Li Szu-Yin Lin He-Sheng Chou Tsung-Yi Chen Yu-An Chen Sheng-Yu Liu Yu-Lin Liu Chiung-An Chen Yen-Cheng Huang Shih-Lun Chen Yi-Cheng Mao Patricia Angela R. Abu Wei-Yuan Chiang Wen-Shen Lo
author_facet	Chun-Wei Li Szu-Yin Lin He-Sheng Chou Tsung-Yi Chen Yu-An Chen Sheng-Yu Liu Yu-Lin Liu Chiung-An Chen Yen-Cheng Huang Shih-Lun Chen Yi-Cheng Mao Patricia Angela R. Abu Wei-Yuan Chiang Wen-Shen Lo
author_sort	Chun-Wei Li
title	Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
title_short	Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
title_full	Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
title_fullStr	Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
title_full_unstemmed	Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
title_sort	detection of dental apical lesions using cnns on periapical radiograph
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dd261695f058410fb798223364d76d80
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Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

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