Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study
한성훈, 임지섭, 김준식, 조진형, 홍미희, 김민지, 김수정, 김윤지, 김영호, 임성훈, 성상진, 강경화, 백승학, 최성권, 김남국,
소속 상세정보
한성훈 ( Han Sung-Hoon ) -
임지섭 ( Lim Ji-Sup ) -
김준식 ( Kim Jun-Sik ) -
조진형 ( Cho Jin-Hyoung ) - Chonnam National University School of Dentistry Department of Orthodontics
홍미희 ( Hong Mi-Hee ) - Kyungpook National University School of Dentistry Department of Orthodontics
김민지 ( Kim Min-Ji ) -
김수정 ( Kim Su-Jung ) -
김윤지 ( Kim Yoon-Ji ) -
김영호 ( Kim Young-Ho ) -
임성훈 ( Lim Sung-Hoon ) - Chosun University College of Dentistry Department of Orthodontics
성상진 ( Sung Sang-Jin ) -
강경화 ( Kang Kyung-Hwa ) - Wonkwang University School of Dentistry Department of Orthodontics
백승학 ( Baek Seung-Hak ) -
최성권 ( Choi Sung-Kwon ) - Wonkwang University School of Dentistry Department of Orthodontics
김남국 ( Kim Nam-Kug ) -
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.
키워드
Artificial intelligence; Convolutional neural network; Posteroanterior cephalograms
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