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

Sung Hoon Han, Jisup Lim, Jun Sik Kim, Jin Hyoung Cho, Mihee Hong, Minji Kim, Su Jung Kim, Yoon Ji Kim, Young Ho Kim, Sung Hoon Lim, Sang Jin Sung, Kyung Hwa Kang, Seung Hak Baek, Sung Kwon Choi, Namkug Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)48-58
Number of pages11
JournalKorean Journal of Orthodontics
Volume54
Issue number1
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Korean Association of Orthodontists.

Keywords

  • Artificial intelligence
  • Convolutional neural network
  • Posteroanterior cephalograms

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