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KJO Korean Journal of Orthodontics

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Korean J Orthod

Published online March 11, 2022

Copyright © The Korean Association of Orthodontists.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery

Mihee Hong a, Inhwan Kim b, Jin-Hyoung Cho c, Kyung-Hwa Kang d, Minji Kim e, Su-Jung Kim f, Yoon-Ji Kim g, Sang-Jin Sung h, Young Ho Kim i, Sung-Hoon Lim j, Namkug Kim k, Seung-Hak Baek l

a PhD student, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, and Assistant Professor, Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
b Graduate student (PhDc), Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
c Professor, Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Republic of Korea
d Professor, Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan-si, Republic of Korea
e Associate Professor, Department of Orthodontics, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
f Professor, Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Republic of Korea
g Assistant Professor, Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
h Professor, Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
i Professor, Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon-si, Gyeonggi-do, Republic of Korea
j Professor, Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Republic of Korea
k Professor, Department of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical Center, Seoul, Republic of Korea
l Professor, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Republic of Korea

Correspondence to:Co-First Authors:
· Mihee Hong, Department of Orthodontics, School of Dentistry, Kyungpook National University, 2175, Dalgubeoldae-ro, Daegu, 41940, Republic of Korea, Tel: +82-53-600-7374 Fax:+82-53-421-4925 e-mail: mhhong1208@gmail.com; Orcid number: 0000-0001-6015-1482
· Inhwan Kim, Department of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea; Tel:+82-2-3260-2014; Fax: +82-2-476-4719; e-mail: krh24711@gmail.com; Orcid number: 0000-0001-7847-6875
Co-corresponding Authors:
· Seung-Hak Baek, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Daehak-no #101, Jongno-gu, Seoul, 03080, Republic of Korea,
Tel:+82-2-2072-3952, Fax:+82-2-3672-2678, e-mail: drwhite@unitel.co.kr; Orcid number: 0000-0002-6586-9503
· Namkug Kim, Department of Convergence Medicine, Department of Convergence Medicine,
University of Ulsan, College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, Tel:+82-2-3010-6573; Fax:+82-2-3010-6196; e-mail: namkugkim@gmail.com; Orcid number: 0000-0002-3438-2217

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective: To investigate the pattern of accuracy change in artificial intelligence (AI)-assisted landmark identification (LI) in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery using a convolutional neural network (CNN) algorithm.
Materials and Methods: 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n=23 per group) for LI using a CNN model. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery [T1, presence of orthodontic brackets (OBs)], post-surgery [T2, presence of OBs and surgical plates and screws (SPS)], and debonding [T3, presence of S-PS and fixed retainers (FR)]. After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed.
Results: The total mean error was 1.17 mm without significant difference among four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points [(T0, T1) vs. (T2, T3)], ANS, A point, and B point showed an increase in error (P < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showed a decrease in error (all P < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups.
Conclusion: The CNN model can be used for LI in serial Lat-cephs despite presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Keywords: CNN, landmark identification

Article

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Korean J Orthod

Published online March 11, 2022

Copyright © The Korean Association of Orthodontists.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery

Mihee Hong a, Inhwan Kim b, Jin-Hyoung Cho c, Kyung-Hwa Kang d, Minji Kim e, Su-Jung Kim f, Yoon-Ji Kim g, Sang-Jin Sung h, Young Ho Kim i, Sung-Hoon Lim j, Namkug Kim k, Seung-Hak Baek l

a PhD student, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, and Assistant Professor, Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
b Graduate student (PhDc), Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
c Professor, Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Republic of Korea
d Professor, Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan-si, Republic of Korea
e Associate Professor, Department of Orthodontics, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
f Professor, Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Republic of Korea
g Assistant Professor, Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
h Professor, Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
i Professor, Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon-si, Gyeonggi-do, Republic of Korea
j Professor, Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Republic of Korea
k Professor, Department of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical Center, Seoul, Republic of Korea
l Professor, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Republic of Korea

Correspondence to:Co-First Authors:
· Mihee Hong, Department of Orthodontics, School of Dentistry, Kyungpook National University, 2175, Dalgubeoldae-ro, Daegu, 41940, Republic of Korea, Tel: +82-53-600-7374 Fax:+82-53-421-4925 e-mail: mhhong1208@gmail.com; Orcid number: 0000-0001-6015-1482
· Inhwan Kim, Department of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea; Tel:+82-2-3260-2014; Fax: +82-2-476-4719; e-mail: krh24711@gmail.com; Orcid number: 0000-0001-7847-6875
Co-corresponding Authors:
· Seung-Hak Baek, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Daehak-no #101, Jongno-gu, Seoul, 03080, Republic of Korea,
Tel:+82-2-2072-3952, Fax:+82-2-3672-2678, e-mail: drwhite@unitel.co.kr; Orcid number: 0000-0002-6586-9503
· Namkug Kim, Department of Convergence Medicine, Department of Convergence Medicine,
University of Ulsan, College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, Tel:+82-2-3010-6573; Fax:+82-2-3010-6196; e-mail: namkugkim@gmail.com; Orcid number: 0000-0002-3438-2217

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective: To investigate the pattern of accuracy change in artificial intelligence (AI)-assisted landmark identification (LI) in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery using a convolutional neural network (CNN) algorithm.
Materials and Methods: 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n=23 per group) for LI using a CNN model. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery [T1, presence of orthodontic brackets (OBs)], post-surgery [T2, presence of OBs and surgical plates and screws (SPS)], and debonding [T3, presence of S-PS and fixed retainers (FR)]. After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed.
Results: The total mean error was 1.17 mm without significant difference among four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points [(T0, T1) vs. (T2, T3)], ANS, A point, and B point showed an increase in error (P < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showed a decrease in error (all P < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups.
Conclusion: The CNN model can be used for LI in serial Lat-cephs despite presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Keywords: CNN, landmark identification