Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™ radiographic scoring system.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
Published Date: Jul 2, 2022

Abstract

OBJECTIVE: To evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™) radiographic scoring system (RSS).

Authors

  • Wannakamon Panyarak
    Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand. Electronic address: wannakamon.p@cmu.ac.th.
  • Kittichai Wantanajittikul
    Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
  • Wattanapong Suttapak
    Division of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand.
  • Arnon Charuakkra
    Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand.
  • Sangsom Prapayasatok
    Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand.

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