Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning.

Journal: Bioengineering (Basel, Switzerland)
Published Date: May 15, 2025

Abstract

In the dentistry field, dental caries is a common issue affecting all age groups. The presence of dental braces and dental restoration makes the detection of caries more challenging. Traditionally, dentists rely on visual examinations to diagnose caries under restoration and dental braces, which can be prone to errors and are time-consuming. This study proposes an innovative deep learning and image processing-based approach for automated caries detection under restoration and dental braces, aiming to reduce the clinical burden on dental practitioners. The contributions of this research are summarized as follows: (1) YOLOv8 was employed to detect individual teeth in bitewing radiographs, and a rotation-aware segmentation method was introduced to handle angular variations in BW. The method achieved a sensitivity of 99.40% and a recall of 98.5%. (2) Using the original unprocessed images, AlexNet achieved an accuracy of 95.83% for detecting caries under restoration and dental braces. By incorporating the image processing techniques developed in this study, the accuracy of Inception-v3 improved to a maximum of 99.17%, representing a 3.34% increase over the baseline. (3) In clinical evaluation scenarios, the proposed AlexNet-based model achieved a specificity of 99.94% for non-caries cases and a precision of 99.99% for detecting caries under restoration and dental braces. All datasets used in this study were obtained with IRB approval (certificate number: 02002030B0). A total of 505 bitewing radiographs were collected from Chang Gung Memorial Hospital in Taoyuan, Taiwan. Patients with a history of the human immunodeficiency virus (HIV) were excluded from the dataset. The proposed system effectively identifies caries under restoration and dental braces, strengthens the dentist-patient relationship, and reduces dentist time during clinical consultations.

Authors

  • Yi-Cheng Mao
    Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
  • Yuan-Jin Lin
    Department of Program on Semiconductor Manufacturing Technology (PSMT), Academy of Innovative Semiconductor and Sustainable Manufacturing (AISSM), National Cheng Kung University, Tainan City 701401, Taiwan.
  • Jen-Peng Hu
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Zi-Yu Liu
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Shih-Lun Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.
  • Chiung-An Chen
    Department of Electrical Engineering, Ming Chi University of Technology, New Taipei 243303, Taiwan.
  • Tsung-Yi Chen
    Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
  • Kuo-Chen Li
    Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan.
  • Liang-Hung Wang
    Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Wei-Chen Tu
    Department of Electrical Engineering, National Cheng Kung University, Tainan City 701401, Taiwan.
  • Patricia Angela R Abu
    Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon 1108, Philippines.

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