Assessing the Accuracy and Reliability of Fully AI-Driven Cephalometric Analysis in Comparison to Digital Manual Methods
DOI:
https://doi.org/10.54133/ajms.v8i2.1839Keywords:
Artificial intelligence, Cephalometric analysis, WebcephAbstract
Background: Artificial intelligence (AI) has invaded radiographic analysis in a massive way. Besides saving time and effort during decision-making and treatment planning, AI-assisted cephalometric analysis must be reliable, reproducible, accurate, and user-friendly. Objective: To assess fully AI-driven cephalometric analysis. Methods: 47 lateral cephalometric radiographs were used for comparing the accuracy of AI-operated and manually operated skeletal and dental cephalometric analysis. Both dental and skeletal analyses were digitally performed using web-based platforms (WebCephTM, Cephio, and Ceppro DDH Inc., Korea). SPSS was used for statistical analysis with paired t-test and intra-class correlation. Results: There were statistical differences between AI landmarking and manual landmarking regarding SNA, ANB, FH to AB, U1 to FH, U1 to SN, U1 to UOP, interincisal angle, U1 to NA (mm), and U1 to NA (deg). The intraclass correlation coefficient (ICC) data showed that the two sets of measurements were very consistent for most readings. Conclusions: Even though AI provides strong reliability and agreement between methods, the significant difference indicates that AI-operated and manually operated cephalometric analysis may not be interchangeable despite their consistency. AI-based analyses primarily function as assistant tools, and orthodontists need to make judgments or adjustments before making decisions.
Downloads
References
Mercier JP, Rossi C, Sanchez IN, Renovales ID, Sahagún PM, Templier L. Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study. BMC Oral Health. 2024;24(1):1309. doi: 10.1186/s12903-024-05097-6. DOI: https://doi.org/10.1186/s12903-024-05097-6
Narkhede S, Rao P, Sawant V, Sachdev SS, Arora S, Pawar AM, et al. Digital versus manual tracing in cephalometric analysis: A systematic review and meta-analysis. J Pers Med. 2024;14(6):566. doi: 10.3390/jpm14060566. DOI: https://doi.org/10.3390/jpm14060566
Jihed Mh, Dallel I, Tobji S, Amor AB. The impact of artificial intelligence on contemporary orthodontic treatment planning-a systematic review and meta-analysis. Sch J Dent Sci. 2022;5:70-87. doi: 10.36347/sjds.2022.v09i05.001. DOI: https://doi.org/10.36347/sjds.2022.v09i05.001
Hügle M, Omoumi P, van Laar JM, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Pract. 2020;4(1):rkaa005. doi: 10.1093/rap/rkaa005. DOI: https://doi.org/10.1093/rap/rkaa005
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8. DOI: https://doi.org/10.1186/s40537-021-00444-8
Khosravani S, Esmaeili S, Malek Mohammadi N, Eslamian L, Dalaie K, Motamedian SR. Inter and intra-rater reliability of lateral cephalometric analysis using 2D dolphin imaging software. J Dent School. 2020;38(4):148-152. doi: 10.22037/jds.v38i4.35384.
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(1):69-76. doi: 10.2319/022019-129.1. DOI: https://doi.org/10.2319/022019-129.1
Daraqel B, Wafaie K, Mohammed H, Cao L, Mheissen S, Liu Y, et al. The performance of artificial intelligence models in generating responses to general orthodontic questions: ChatGPT vs Google Bard. Am J Orthod Dentofacial Orthop. 2024;165(6):652-662. doi: 10.1016/j.ajodo.2024.01.012. DOI: https://doi.org/10.1016/j.ajodo.2024.01.012
Tsolakis IA, Tsolakis AI, Elshebiny T, Matthaios S, Palomo JM. Comparing a fully automated cephalometric tracing method to a manual tracing method for orthodontic diagnosis. J Clin Med. 2022;11(22):6854. doi: 10.3390/jcm11226854. DOI: https://doi.org/10.3390/jcm11226854
Schwendicke F, Chaurasia A, Arsiwala L, Lee JH, Elhennawy K, Jost-Brinkmann PG, et al. Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clin Oral Investig. 2021;25(7):4299-4309. doi: 10.1007/s00784-021-03990-w. DOI: https://doi.org/10.1007/s00784-021-03990-w
Kim J, Kim I, Kim YJ, Kim M, Cho JH, Hong M, et al. Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res. 2021;24 Suppl 2:59-67. doi: 10.1111/ocr.12493. DOI: https://doi.org/10.1111/ocr.12493
Lee Y, Pyeon JH, Han SH, Kim NJ, Park WJ, Park JB. A comparative study of deep learning and manual methods for identifying anatomical landmarks through cephalometry and cone-beam computed tomography: A systematic review and meta-analysis. Appl Sci. 2024;14(16):7342. doi: 10.3390/app14167342. DOI: https://doi.org/10.3390/app14167342
Ongkosuwito EM, Katsaros C, van 't Hof MA, Bodegom JC, Kuijpers-Jagtman AM. The reproducibility of cephalometric measurements: a comparison of analogue and digital methods. Eur J Orthod. 2002;24(6):655-665. doi: 10.1093/ejo/24.6.655. PMID: 12512783.. DOI: https://doi.org/10.1093/ejo/24.6.655
Sayinsu K, Isik F, Trakyali G, Arun T. An evaluation of the errors in cephalometric measurements on scanned cephalometric images and conventional tracings. Eur J Orthod. 2007;29(1):105-198. doi: 10.1093/ejo/cjl065. DOI: https://doi.org/10.1093/ejo/cjl065
Mahto RK, Kafle D, Giri A, Luintel S, Karki A. Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform. BMC Oral Health. 2022;22(1):132. doi: 10.1186/s12903-022-02170-w. DOI: https://doi.org/10.1186/s12903-022-02170-w
Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. J Stomatol Oral Maxillofac Surg. 2020;121(1):53-57. doi: 10.1016/j.jormas.2019.04.017. DOI: https://doi.org/10.1016/j.jormas.2019.04.017
Meriç P, Naoumova J. Web-based fully automated cephalometric analysis: Comparisons between App-aided, computerized, and manual tracings. Turk J Orthod. 2020;33(3):142-149. doi: 10.5152/TurkJOrthod.2020.20062. DOI: https://doi.org/10.5152/TurkJOrthod.2020.20062
Kang S, Kim I, Kim YJ, Kim N, Baek SH, Sung SJ. Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks. Orthod Craniofac Res. 2024;27(1):64-77. doi: 10.1111/ocr.12683. DOI: https://doi.org/10.1111/ocr.12683

Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Al-Rafidain Journal of Medical Sciences ( ISSN 2789-3219 )

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Published by Al-Rafidain University College. This is an open access journal issued under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/).