Localisation and classification of multi-stage caries on CBCT images with a 3D convolutional neural network.

Journal: Clinical oral investigations
PMID: 40227550

Abstract

OBJECTIVES: Dental caries remains a significant global health concern. Recognising the diagnostic potential of cone-beam computed tomography (CBCT) in caries assessment, this study aimed to develop an artificial intelligence (AI)-driven tool for accurate caries localisation and classification on CBCT images, thereby enhancing early diagnosis and precise treatment planning.

Authors

  • Shuai Qi
  • Yujie Fu
    Department of Endodontics, School and Hospital of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, China.
  • Haoxuan Shan
    College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
  • Genqiang Ren
    College of Electronics and Information Engineering, Tongji University, Shanghai, China.
  • Yufei Chen
    College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China. Electronic address: yufeichen@tongji.edu.cn.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

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