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Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study

  • Sung-Hoon Han (Department of Orthodontics, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jisup Lim (Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jun-Sik Kim (Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jin-Hyoung Cho (Department of Orthodontics, School of Dentistry, Chonnam National University) ;
  • Mihee Hong (Department of Orthodontics, School of Dentistry, Kyungpook National University) ;
  • Minji Kim (Department of Orthodontics, College of Medicine, Ewha Womans University) ;
  • Su-Jung Kim (Department of Orthodontics, Kyung Hee University School of Dentistry) ;
  • Yoon-Ji Kim (Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Young Ho Kim (Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine) ;
  • Sung-Hoon Lim (Department of Orthodontics, College of Dentistry, Chosun University) ;
  • Sang Jin Sung (Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Kyung-Hwa Kang (Department of Orthodontics, School of Dentistry, Wonkwang University) ;
  • Seung-Hak Baek (Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University) ;
  • Sung-Kwon Choi (Department of Orthodontics, School of Dentistry, Wonkwang University) ;
  • Namkug Kim (Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine)
  • Received : 2023.03.31
  • Accepted : 2023.10.10
  • Published : 2024.01.25

Abstract

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

Keywords

Acknowledgement

This research was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health & Welfare (HI18C1638) and the Technology Innovation Program (20006105) funded by the Ministry of Trade, Industry & Energy, Republic of Korea.

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