Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 38155015

Abstract

OBJECTIVE: The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes.

Authors

  • Yiing-Shiuan Huang
    Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA. Electronic address: yiingshiuanhuang@gmail.com.
  • Pavel Iakubovskii
    Denti.AI Technology Inc., Toronto, Ontario, Canada.
  • Li Zhen Lim
    Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA; Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, National University of Singapore, Singapore.
  • AndrĂ© Mol
    Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States.
  • Donald A Tyndall
    Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States.

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