Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm:A multicenter study
- Author:
Sung-Hoon HAN
1
;
Jisup LIM
;
Jun-Sik KIM
;
Jin-Hyoung CHO
;
Mihee HONG
;
Minji KIM
;
Su-Jung KIM
;
Yoon-Ji KIM
;
Young KIM
;
Sung-Hoon LIM
;
Sang Jin SUNG
;
Kyung-Hwa KANG
;
Seung-Hak BAEK
;
Sung-Kwon CHOI
;
Namkug KIM
Author Information
- Publication Type:Original Article
- From:The Korean Journal of Orthodontics 2024;54(1):48-58
- CountryRepublic of Korea
- Language:English
-
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 ANSmid, 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.
