Detection of dental apical lesions using cnns on periapical radiograph

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

*此作品的通信作者

研究成果: Article同行評審

37 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號7049
期刊Sensors
21
發行號21
DOIs
出版狀態Published - 1 11月 2021

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