Agreement and reliability of AI-based cephalometric analysis (WebCeph) compared with digital manual tracing for skeletal and dentoalveolar measurements (A retrospective comparative study)
Main Article Content
Abstract
Background: Cephalometric analysis is essential in orthodontic diagnosis and treatment planning. With the increasing use of artificial intelligence (AI) in clinical practice, automated platforms such as WebCeph have been introduced; however, their accuracy and reliability across different types of measurements remain a concerns. Objectives: evaluate the agreement and reliability between skeletal and dentoalveolar cephalometric measurements obtained using an AI-based platform (WebCeph) and digital manual tracing performed with AutoCAD.Materials and Methods: A total of 100 lateral cephalograms were analyzed using WebCeph (AI-driven software) and digital manual tracing in AutoCAD. Fourteen Steiner and Tweed variables were selected and categorized into skeletal and dentoalveolar measurements. Statistical comparisons were performed using paired t-tests or Wilcoxon signed-rank tests, depending on data distribution. Agreement between the two methods was assessed using intraclass correlation coefficients (ICC) and Bland–Altman analysis. Conclusion: Overall agreement between WebCeph and AutoCAD was good for most variables (ICC ≥ 0.75). Skeletal and dentoalveolar measurements showed comparable reliability, although skeletal measurements demonstrated slightly smaller mean differences and narrower limits of agreement. Greater variability was observed in dentoalveolar variables, particularly those related to mandibular incisor inclination. Conclusion:WebCeph provides clinically reliable cephalometric measurements and can be considered a supportive diagnostic tool. However, selective manual verification is essential.
Received date: 02-02-2026
Accepted date: 23-04-2026
Published date: 15-06-2026
Downloads
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licenses and Copyright
The following policy applies in The Journal of Baghdad College of Dentistry (JBCD):
# JBCD applies the Creative Commons Attribution (CC BY) license to articles and other works we publish. If you submit your paper for publication by JBCD, you agree to have the CC BY license applied to your work. Under this Open Access license, you as the author agree that anyone can reuse your article in whole or part for any purpose, for free, even for commercial purposes. Anyone may copy, distribute, or reuse the content as long as the author and original source are properly cited. This facilitates freedom in re-use and also ensures that JBCD content can be mined without barriers for the needs of research.
# If your manuscript contains content such as photos, images, figures, tables, audio files, videos, etc., that you or your co-authors do not own, we will require you to provide us with proof that the owner of that content (a) has given you written permission to use it, and (b) has approved of the CC BY license being applied to their content. We provide a form you can use to ask for and obtain permission from the owner. If you do not have owner permission, we will ask you to remove that content and/or replace it with other content that you own or have such permission to use.Don't assume that you can use any content you find on the Internet, or that the content is fair game just because it isn't clear who the owner is or what license applies.
# Many authors assume that if they previously published a paper through another publisher, they own the rights to that content and they can freely use that content in their paper, but that’s not necessarily the case, it depends on the license that covers the other paper. Some publishers allow free and unrestricted re-use of article content they own, such as under the CC BY license. Other publishers use licenses that allow re-use only if the same license is applied by the person or publisher re-using the content. If the paper was published under a CC BY license or another license that allows free and unrestricted use, you may use the content in your JBCD paper provided that you give proper attribution, as explained above.If the content was published under a more restrictive license, you must ascertain what rights you have under that license. At a minimum, review the license to make sure you can use the content. Contact that JBCD if you have any questions about the license. If the license does not permit you to use the content in a paper that will be covered by an unrestricted license, you must obtain written permission from the publisher to use the content in your JBCD paper. Please do not include any content in your JBCD paper which you do not have rights to use, and always give proper attribution.
# If any relevant accompanying data is submitted to repositories with stated licensing policies, the policies should not be more restrictive than CC BY.
# JBCD reserves the right to remove any photos, captures, images, figures, tables, illustrations, audio and video files, and the like, from any paper, whether before or after publication, if we have reason to believe that the content was included in your paper without permission from the owner of the content.
How to Cite
References
Forsyth DB, Shaw WC, Richmond S, Roberts CT. Digital imaging of cephalometric radiographs, part 2: image quality. The Angle Ortho. 1996 Feb 1;66(1):43-50.
Zaheer R, Shafique HZ, Khalid Z, Shahid R, Jan A, Zahoor T, et al. Comparison of semi- and fully automated artificial intelligence-driven software and manual systems for cephalometric analysis. BMC Med Inform Decis Mak. 2024;24(1):271. DOI: https://doi.org/10.1186/s12911-024-02664-3
Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res. 2021;100(3):232–244. DOI: https://doi.org/10.1177/0022034520969115
Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry: A systematic review. J Dent Sci. 2021;16(1):508–522. DOI: https://doi.org/10.1016/j.jds.2020.06.019
Tandon D, Rajawat J, Banerjee M. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res. 2020;10(4):391–396. DOI: https://doi.org/10.1016/j.jobcr.2020.07.015
Kong SC, Cheung WMY, Zhang G. Evaluating artificial intelligence literacy courses for fostering conceptual learning and empowerment in university students. Comput Hum Behav Rep. 2022;7:100223. DOI: https://doi.org/10.1016/j.chbr.2022.100223
Lee RS. Artificial Intelligence in Daily Life. Springer; 2020. DOI: https://doi.org/10.1007/978-981-15-7695-9
Liermann VJ. Overview of machine learning and deep learning frameworks. J Big Data. 2021;8:187–224. DOI: https://doi.org/10.1007/978-3-030-78821-6_12
Yassir YA, Salman AR, Nabbat SA. The accuracy and reliability of WebCeph for cephalometric analysis. J Taibah Univ Med Sci. 2021;17(1):57–66.
Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. J Stomatol Oral Maxillofac Surg. 2020;121(1):53–57. DOI: https://doi.org/10.1016/j.jormas.2019.04.017
Houston WJ. The analysis of errors in orthodontic measurements. Am J Orthod. 1983;83(5):382–390. DOI: https://doi.org/10.1016/0002-9416(83)90322-6
Histau B, Coreil M, Chapple A, Armbruster P, Ballard R. Comparison of AudaxCeph® fully automated cephalometric tracing technology to a semiautomated approach. Int Orthod. 2022;20:100597. DOI: https://doi.org/10.1016/j.ortho.2022.100691
Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of artificial intelligence in cephalometric analysis: A narrative review. Diagnostics (Basel). 2023;13(16):2704. DOI: https://doi.org/10.3390/diagnostics13162640
Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J. Assessment of different AI-based providers for automated cephalometric analysis. J Orofac Orthop. 2023.
Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MN, Donatelli RE, Lee SJ. Automated identification of cephalometric landmarks: Part 2. Angle Orthod. 2020;90:69–76. DOI: https://doi.org/10.2319/022019-129.1
Indermun S, Shaik S, Nyirenda C, Johannes K, Mulder R. Human examination and artificial intelligence in cephalometric landmark detection. Dentomaxillofac Radiol. 2023;52:20230012. DOI: https://doi.org/10.1259/dmfr.20220362
Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, Vandaele R, Marée R, Jodogne S, Geurts P, Chen C.. Evaluation of anatomical landmark detection methods for cephalometric X-ray images. IEEE Trans Med Imaging. 2015;34:1890–1900. DOI: https://doi.org/10.1109/TMI.2015.2412951
Katyal BN. Evaluation of the accuracy and reliability of WebCeph: An AI-based online software. APOS Trends Orthod. 2022. DOI: https://doi.org/10.25259/APOS_138_2021
Mahto RK, Kafle D, Giri A, Luintel S, Karki A. Evaluation of fully automated cephalometric measurements obtained from a web-based AI platform. BMC Oral Health. 2022;22(1):132. DOI: https://doi.org/10.1186/s12903-022-02170-w
Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in orthodontics: Revolutionizing diagnostics and treatment planning—A comprehensive review. J clin med. 2024;13(2):344. DOI: https://doi.org/10.3390/jcm13020344
Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, et al. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health. 2023;23:456. DOI: https://doi.org/10.1186/s12903-023-02881-8
Silva TP, Pinheiro MCR, Freitas DQ, Gaeta-Araujo H, Oliveira-Santos C. Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;137(4):431–440. DOI: https://doi.org/10.1016/j.oooo.2024.01.011
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;99:249–256. DOI: https://doi.org/10.1177/0022034520901715
Alessandri-Bonetti A, Sangalli L, Salerno M, Gallenzi P. Reliability of artificial intelligence-assisted cephalometric analysis. BioMedInformatics. 2023;3:44–53. DOI: https://doi.org/10.3390/biomedinformatics3010003
Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod. 2021;91:329–335. DOI: https://doi.org/10.2319/021220-100.1
Paixão MB, Sobral MC, Vogel CJ, Araujo TM.. Comparative study between manual and digital cephalometric tracing using Dolphin Imaging software with lateral radiographs. Dental Press J Orthod. 2010;15(6):123–130. DOI: https://doi.org/10.1590/S2176-94512010000600016
Bruntz LQ, Palomo JM, Baden S, Hans MG. A comparison of scanned lateral cephalograms with corresponding original radiographs. Am J Orthod Dentofacial Orthop. 2006;130(3):340–348. DOI: https://doi.org/10.1016/j.ajodo.2004.12.029
Uysal T, Baysal A, Yagci A. Evaluation of speed, repeatability, and reproducibility of digital radiography with manual versus computer-assisted cephalometric analyses. Eur J Orthod. 2009;31(5):523–528. DOI: https://doi.org/10.1093/ejo/cjp022
Singh P, Davies TI. A comparison of cephalometric measurements: a picture archiving and communication system versus the hand-tracing method. Eur J Orthod. 2011;33(4):350–353. DOI: https://doi.org/10.1093/ejo/cjq087
Gregston MD, Kula T, Hardman P, Glaros A, Kula K.. Comparison of conventional and digital radiographic methods and cephalometric analysis software. Semin Orthod. 2004;10(3):204–211. DOI: https://doi.org/10.1053/j.sodo.2004.05.004
Celik E, Polat-Ozsoy O, Toygar Memikoglu TU. Comparison of cephalometric measurements with digital versus conventional cephalometric analysis. Eur J Orthod. 2009;31(3):241–246. DOI: https://doi.org/10.1093/ejo/cjn105
Agarwal N, Bagga DK, Sharma P. A comparative study of cephalometric measurements with digital versus manual methods. J Indian Orthod Soc. 2011;45(2):84–90. DOI: https://doi.org/10.5005/jp-journals-10021-1014
Polat-Ozsoy O, Gokcelik A, Toygar Memikoglu TU. Differences in cephalometric measurements: a comparison of digital versus hand-tracing methods. Eur J Orthod. 2009;31(3):254–259. DOI: https://doi.org/10.1093/ejo/cjn121
Moreno M, Gebeile-Chauty S. Comparative study of two software for cephalometric landmark detection. Orthod Fr. 2022;93(1):41–61. DOI: https://doi.org/10.1684/orthodfr.2022.73
Yassir YA, Salman AR, Nabbat SA. The accuracy and reliability of WebCeph for cephalometric analysis. J Taibah Univ Med Sci. 2021;17(1):57–66 DOI: https://doi.org/10.1016/j.jtumed.2021.08.010