Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.

Journal: BMC oral health
PMID: 39333975

Abstract

BACKGROUND: This study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.

Authors

  • Li Ye
    Guangxi Key Laboratory of AIDS Prevention and Treatment & Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, School of Public Health, Guangxi Medical University, Nanning 530021, Guangxi, China.
  • Shangxuan Li
    Hanglok-Tech Co.,Ltd, Zhuhai, China.
  • Chichi Li
    Hanglok-Tech Co.,Ltd, Zhuhai, China.
  • Cheng Wang
    Department of Pathology, Dalhousie University, Halifax, NS, Canada.
  • Xi Wei
    Department of Diagnostic and Therapeutic Ultrasonography, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Wu Zhou
    School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 510006.
  • Yu Du
    State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.

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