A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs.

Journal: BMC oral health
Published Date: May 26, 2025

Abstract

PURPOSE: Numerous studies have investigated the use of convolutional neural network (CNN) models for detecting periapical lesions(PLs). However, limited research has focused on evaluating their potential in assisting clinicians with diagnosis. This study aims to utilize two deep learning(DL) models, ConvNeXt and ResNet34, to aid novice dentists in the detection of PLs on periapical radiographs (PRs). By assessing the diagnostic support provided by these models, this research seeks to promote the clinical application of DL in dentistry.

Authors

  • Jian Liu
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Chaoran Jin
    School of Stomatology, Shandong Second Medical University, Weifang, 261053, Shandong, China.
  • Xiaolan Wang
    School of Electrical and Electronic Information, Xihua University, Chengdu, China.
  • Kexu Pan
    School of Stomatology, Shandong Second Medical University, Weifang, 261053, Shandong, China.
  • Zhuoyang Li
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
  • Xinxuan Yi
    School of Stomatology, Shandong Second Medical University, Weifang, 261053, Shandong, China.
  • Yu Shao
    Department of Leisure Sport, Shanghai University of Sport, Shanghai 200438, China.
  • Xiaodong Sun
    Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai JiaoTong University, 200080 Shanghai, China.
  • Xijiao Yu
    School of Stomatology, Shandong Second Medical University, Weifang, 261053, Shandong, China. yayiyu@163.com.

Keywords

External Resources

Popular Topics
Recent Journals