Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

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.

Authors

  • Chun-Wei Li
    Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Szu-Yin Lin
    Department of Computer Science and Information Engineering, National Ilan University, Yilan City 260, Taiwan.
  • He-Sheng Chou
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Tsung-Yi Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Yu-An Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Sheng-Yu Liu
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Yu-Lin Liu
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Chiung-An Chen
    Department of Electrical Engineering, Ming Chi University of Technology, New Taipei 243303, Taiwan.
  • Yen-Cheng Huang
    Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Shih-Lun Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.
  • Yi-Cheng Mao
    Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Patricia Angela R Abu
    Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon 1108, Philippines.
  • Wei-Yuan Chiang
    National Synchrotron Radiation Research Center, Hsinchu City 30076, Taiwan.
  • Wen-Shen Lo
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.