An Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network.

Journal: Journal of endodontics
PMID: 37019378

Abstract

INTRODUCTION: This study aimed to evaluate the use of deep convolutional neural network (DCNN) algorithms to detect clinical features and predict the three-year outcome of endodontic treatment on preoperative periapical radiographs.

Authors

  • Junghoon Lee
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Hyunseok Seo
    Medical Physics Division in the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, 94305-5847, USA.
  • Yoon Jeong Choi
    Department of Orthodontics, The Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea.
  • Chena Lee
    Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
  • Sunil Kim
    Microscope Center, Department of Conservative Dentistry and Oral Science Research Center, Yonsei University College of Dentistry, Seoul, Korea.
  • Ye Sel Lee
    Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, Korea.
  • Sukjoon Lee
    Oral Science Research Center, Yonsei University College of Dentistry, Seoul, Korea.
  • Euiseong Kim
    Microscope Center, Department of Conservative Dentistry and Oral Science Research Center, Yonsei University College of Dentistry, Seoul, Korea. Electronic address: andyendo@yuhs.ac.

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