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KJO Korean Journal of Orthodontics

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eISSN 2005-372X
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Korean J Orthod

Published online October 13, 2020

Copyright © The Korean Association of Orthodontists.

Evaluation of multi-stage convolutional neural network based fully automatic landmark identification system using CBCT synthesized posteroanterior cephalometric images

Min-Jung Kima, Yi Liub, Song Hee Ohc, Hyo-Won Ahnd, Seong-Hun Kime*, Gerald Nelsonf

aGraduate student, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
bAssociate Professor, Department of Orthodontics, Peking University School of Stomatology, Beijing, PR China
cClinical Fellow, Department of Oral and Maxillofacial Radiology, Graduate School, Kyung Hee University, Seoul, Korea.
dAssociate Professor, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
eProfessor and Head, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
fProfessor Emeritus, Division of Orthhodontics, Department of Orofacial Science, University of California San Francisco, CA, USA

Correspondence to:Seong-Hun Kim D.M.D., M.S.D., Ph.D.
Department of Orthodontics, Graduate School, Kyung Hee University, #1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea
Telephone: 82-2-958-9392
E-mail address: bravortho@gmail.com

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: The aim of this study was to evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification of posteroanterior (PA) cephalometric landmarks Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 synthesized PA cephalograms from conebeam computed tomography (CBCT-PA) were selected as samples. Twenty-three landmarks were manually identified on the all CBCT-PA images by a single examiner. Intra examiner’s reproducibility was confirmed by two times identification on the 85 randomly selected CBCT-PA images which also set as test data with two-week interval before model training. We input the 345 data out of 430 CBCT-PA images to the multi-stage CNN model at the initial learning stages. The multi-stage CNN model was tested with 85 images. The first manual identification on 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors between manual identification and AI prediction. Results: The accuracy of AI showed MRE = 2.23 mm ± 2.02 mm and range of 2 mm below SDR achieved 60.88%. For comparison of the repetitive task, AI predicted landmarks on the same position, and MRE in two times manual identification showed 1.31 ± 0.94 mm. Conclusion: The prediction for CBCT synthesized PA cephalometric landmarks with automated identification has not sufficiently achieved less than 2 mm error range of clinically favorable level. Consistency of AI landmark identification on PA cephalograms was better than manual identification

Keywords: CT, Artificial intelligence, Convolutional Neural Networks, Diagnosis and Treatment planning, Facial asymmetry, Automatic identification, PA Cephalometrics, Cone-Beam Computed Tomography

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Korean J Orthod

Published online October 13, 2020

Copyright © The Korean Association of Orthodontists.

Evaluation of multi-stage convolutional neural network based fully automatic landmark identification system using CBCT synthesized posteroanterior cephalometric images

Min-Jung Kima, Yi Liub, Song Hee Ohc, Hyo-Won Ahnd, Seong-Hun Kime*, Gerald Nelsonf

aGraduate student, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
bAssociate Professor, Department of Orthodontics, Peking University School of Stomatology, Beijing, PR China
cClinical Fellow, Department of Oral and Maxillofacial Radiology, Graduate School, Kyung Hee University, Seoul, Korea.
dAssociate Professor, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
eProfessor and Head, Department of Orthodontics, Graduate School, Kyung Hee University, Seoul, Korea.
fProfessor Emeritus, Division of Orthhodontics, Department of Orofacial Science, University of California San Francisco, CA, USA

Correspondence to:Seong-Hun Kim D.M.D., M.S.D., Ph.D.
Department of Orthodontics, Graduate School, Kyung Hee University, #1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea
Telephone: 82-2-958-9392
E-mail address: bravortho@gmail.com

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: The aim of this study was to evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification of posteroanterior (PA) cephalometric landmarks Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 synthesized PA cephalograms from conebeam computed tomography (CBCT-PA) were selected as samples. Twenty-three landmarks were manually identified on the all CBCT-PA images by a single examiner. Intra examiner’s reproducibility was confirmed by two times identification on the 85 randomly selected CBCT-PA images which also set as test data with two-week interval before model training. We input the 345 data out of 430 CBCT-PA images to the multi-stage CNN model at the initial learning stages. The multi-stage CNN model was tested with 85 images. The first manual identification on 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors between manual identification and AI prediction. Results: The accuracy of AI showed MRE = 2.23 mm ± 2.02 mm and range of 2 mm below SDR achieved 60.88%. For comparison of the repetitive task, AI predicted landmarks on the same position, and MRE in two times manual identification showed 1.31 ± 0.94 mm. Conclusion: The prediction for CBCT synthesized PA cephalometric landmarks with automated identification has not sufficiently achieved less than 2 mm error range of clinically favorable level. Consistency of AI landmark identification on PA cephalograms was better than manual identification

Keywords: CT, Artificial intelligence, Convolutional Neural Networks, Diagnosis and Treatment planning, Facial asymmetry, Automatic identification, PA Cephalometrics, Cone-Beam Computed Tomography