The korean journal of orthodontics (대한치과교정학회지)

The Korean Association Of Orthodontists (대한치과교정학회)
권호 표지
DOI QR코드

DOI QR Code

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
Download PDF
⟨ Previous Next ⟩

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.

References

  1. McCarthy J. Artificial intelligence, logic and formalizing common sense. In: Thomason RH, ed. Philosophical logic and artificial intelligence. Dordrecht: Springer; 1989. p. 161-90. https://doi.org/10.1007/978-94-009-2448-2_6
  2. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69S:S36-40. https://doi.org/10.1016/j.metabol.2017.01.011
  3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44. https://doi.org/10.1038/nature14539
  4. Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol 2018;36:257-72. https://doi.org/10.1007/s11604-018-0726-3
  5. Broadbent BH. A new x-ray technique and its application to orthodontia. Angle Orthod 1931;1:45-66. https://meridian.allenpress.com/angle-orthodontist/article/1/2/45/55162/A-NEW-X-RAY-TECHNIQUEand-ITS-APPLICATION-TO
  6. Kazandjian S, Kiliaridis S, Mavropoulos A. Validity and reliability of a new edge-based computerized method for identification of cephalometric landmarks. Angle Orthod 2006;76:619-24. https://pubmed.ncbi.nlm.nih.gov/16808568/
  7. Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res 2020;99:249-56. https://doi.org/10.1177/0022034520901715
  8. Kim IH, Kim YG, Kim S, Park JW, Kim N. Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net. Sci Rep 2021;11:7925. https://doi.org/10.1038/s41598-021-87261-4
  9. Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, et al. Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge. IEEE Trans Med Imaging 2015;34:1890-900. https://doi.org/10.1109/TMI.2015.2412951
  10. Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, et al. Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod 2019;89:903-9. https://doi.org/10.2319/022019-127.1
  11. Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, et al. Automated identification of cephalometric landmarks: Part 2-Might it be better than human? Angle Orthod 2020;90:69-76. https://doi.org/10.2319/022019-129.1
  12. Song Y, Qiao X, Iwamoto Y, Chen YW. Automatic cephalometric landmark detection on xray images using a deep-learning method. Appl Sci 2020;10:2547. https://doi.org/10.3390/app10072547
  13. Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop 2020;81:52-68. https://doi.org/10.1007/s00056-019-00203-8
  14. Hong M, Kim I, Cho JH, Kang KH, Kim M, Kim SJ, et al. Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery. Korean J Orthod 2022;52:287-97. https://doi.org/10.4041/kjod21.248
  15. Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, et al. Frontal cephalometric landmarking: humans vs artificial neural networks. Int J Comput Dent 2020;23:139-48. https://pubmed.ncbi.nlm.nih.gov/32555767/
  16. Gil SM, Kim I, Cho JH, Hong M, Kim M, Kim SJ, et al. Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am J Orthod Dentofacial Orthop 2022;161:e361-71. https://doi.org/10.1016/j.ajodo.2021.11.011
  17. Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 2020;42:318-27. https://doi.org/10.1109/TPAMI.2018.2858826
  18. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer; 2015. p. 234-41. https://doi.org/10.1007/978-3-319-24574-4_28
  19. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Paper presented at: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2016. https://doi. org/10.1109/CVPR.2016.90
  20. Lee HJ, Lee S, Lee EJ, Song IJ, Kang BC, Lee JS, et al. A comparative study of the deviation of the menton on posteroanterior cephalograms and three-dimensional computed tomography. Imaging Sci Dent 2016;46:33-8. https://doi.org/10.5624/isd.2016.46.1.33
  21. Major PW, Johnson DE, Hesse KL, Glover KE. Landmark identification error in posterior anterior cephalometrics. Angle Orthod 1994;64:447-54. https://pubmed.ncbi.nlm.nih.gov/7864466/
  22. Na ER, Aljawad H, Lee KM, Hwang HS. A comparative study of the reproducibility of landmark identification on posteroanterior and anteroposterior cephalograms generated from cone-beam computed tomography scans. Korean J Orthod 2019;49:41-8. https://doi.org/10.4041/kjod.2019.49.1.41