Development of cephalometric radiography in orthodontic imaging: a literature review
Abstract
Objectives: This review article aims to discuss the development of lateral cephalometric radiography use in science until now.Review: The search for studies on the identification of lateral cephalometric anatomical landmarks based on artificial intelligence was conducted by involving four databases: PubMed, IEEE Xplore, Google Scholar, and Scopus. The article selection was conducted using the keywords "Cephalometric Radiograph," "Automatic Cephalometric," "Cephalometric Landmarking," and "Cephalometric Digital" from January 2000 to March 2022. A total of 11 articles were obtained for this study. Cephalometric radiography is a radiographic technique that shows a picture of the skull and is widely used in dentistry to analyze and assess the relationship between teeth, jaws, and facial bones. Cephalometric analysis can be done by identifying anatomical landmark points and measuring angles on lateral cephalometric radiographs. The development of cephalometric radiography in biomedical imaging, especially in terms of the processing of cephalometric radiograph images from the process of forming X-rays to their potential use in the process of determining automatic anatomical landmark points.
Conclusion: The results of the literature review of the development of dental radiology, especially digital cephalometric radiography, continue to increase, and its development is supported by computing technology, especially Artificial Intelligence.
Keywords: Lateral cephalometric; biomedical imaging; artificial intelligence
References
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23. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. International Journal of Computer Assisted Radiology and Surgery. 2015;10(11):1737-52
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25. Cohen AM, Ip HHS, Linney AD. A Preliminary Study of Computer Recognition and Identification of Skeletal Landmarks as a New Method of Cephalometric Analysis. British Journal of Orthodontics. 1984;11(3):143-54
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28. Lindner C, Wang C-W, Huang C-T, Li C-H, Chang S-W, Cootes TF. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Scientific Reports. 2016;6(1):1-7
29. Wang S, Li H, Li J, Zhang Y, Zou B. Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting. J Healthc Eng. 2018:1-15
30. Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 2017;4(1):1-12
31. Kim H, Shim E, Park J, Kim Y-J, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Computer Methods and Programs in Biomedicine. 2020;194:1-10
32. Lee JH, Yu HJ, Kim MJ, Kim JW, Choi J. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health. 2020;20(1):270
33. Lee M, Chung M, Shin YG. Cephalometric landmark detection via global and local encoders and patch-wise attentions. Neurocomputing. 2022;470:182-9
34. Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W. Automatic localization of cephalometric landmarks based on convolutional neural network. American Journal of Orthodontics and Dentofacial Orthopedics. 2022;161(3):250-9
35. Alessandri-Bonetti A, Sangalli L., Salerno M, Gallenzi P. Reliability of Artificial Intelligence-Assisted Cephalometric Analysis. A Pilot Study. BioMedInformatics. 2023;3(1),44-53
2. Mallya SM, Lam EWN. White and Pharoah's oral radiology : Principles and interpretation. 8th ed. St. Louis, Missouri: Elsevier; 2019. p.389-416
3. Whaites E, Drage N. Essentials of dental radiography and radiology. Sixth edition ed. Edinburgh: Elsevier; 2021. p.363-70
4. 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. European Journal of Orthodontics. 2002;24(6):655-65
5. Mosleh MAA, Baba MS, Himazian N, Makramani BMAA-, editors. An image processing system for cephalometric analysis and measurements. In: Proceedings of the 2008 International Symposium on Information Technology; 2008 Aug 26-28; Kuala Lumpur, Malaysia. New York: IEEE;2005. p.1-8
6. Kamoen A, Dermaut L, Verbeeck R. The clinical significance of error measurement in the interpretation of treatment results. European Journal of Orthodontics. 2001;23(5):569-78
7. Meriç P, Naoumova J. Web-based Fully Automated Cephalometric Analysis: Comparisons between App-aided, Computerized, and Manual Tracings. Turk J Orthod. 2020;33(3):142-9
8. Juneja M, Garg P, Kaur R, Manocha P, Prateek, Batra S, et al. A review on cephalometric landmark detection techniques. Biomedical Signal Processing and Control. 2021;66:102486
9. Durão AR, Pittayapat P, Rockenbach MIB, Olszewski R, Ng S, Ferreira AP, Jacobs R. Validity of 2D lateral cephalometry in orthodontics: a systematic review. Prog Orthod. 2013;14(1):31
10. Keim RG, Gottlieb EL, Vogels DS, 3rd, Vogels PB. 2014 JCO study of orthodontic diagnosis and treatment procedures, Part 1: results and trends. J Clin Orthod. 2014;48(10):607-30
11. Alessandri-Bonetti A, Sangalli L, Salerno M, Gallenzi P. Reliability of Artificial Intelligence-Assisted Cephalometric Analysis. A Pilot Study. BioMedInformatics. 2023; 3(1):44-53
12. Proffit WR, Fields HW, Larson BE, Sarver DM. Contemporary orthodontics. 6th ed. Philadelphia, PA: Elsevier; 2019. p. 140-215
13. Jacobson A, Jacobson RL. Radiographic cephalometry : from basics to 3-D imaging. 2nd ed. Chicago: Quintessence Pub.; 2006. p. 39-40
14. Prince JL, Links JM. Medical imaging signals and systems. 2nd ed. Upper Saddle River, NJ: Pearson Education; 2015. p. 150-154
15. Iannucci JM, Howerton LJ. Dental radiography : a workbook and laboratory manual. 6th ed. St. Louis, Missouri: Elsevier; 2023. p.262
16. Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G. Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images. Sensors. 2021; 21(2): 505
17. Yun HS, Hyun CM, Baek SH, Lee S-H, Seo JK. Automated 3D cephalometric landmaridentification using computerized tomography. Computer Vision and Pattern Recognition. 2021:1-20
18. Rajaraman V. JohnMcCarthy — Father of artificial intelligence. Resonance. 2014;19(3):198-207
19. 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):105-8
20. Subramanian AK, Chen Y, Almalki A, Sivamurthy G, Kafle D. Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. Biomed Res Int. 2022;2022(41) :1880113
21. Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2021;51(1):1-12
22. Alqahtani H. Evaluation of an online website-based platform for cephalometric analysis. Journal of Stomatology, Oral and Maxillofacial Surgery. 2020;121(1):53-7
23. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. International Journal of Computer Assisted Radiology and Surgery. 2015;10(11):1737-52
24. 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(6):903-9
25. Cohen AM, Ip HHS, Linney AD. A Preliminary Study of Computer Recognition and Identification of Skeletal Landmarks as a New Method of Cephalometric Analysis. British Journal of Orthodontics. 1984;11(3):143-54
26. Lévy-Mandel AD, Venetsanopoulos AN, Tsotsos JK. Knowledge-based landmarking of cephalograms. Computers and Biomedical Research. 1986;19(3):282-309
27. Uchino E, Yamakawa T, editors. High speed fuzzy learning machine with guarantee of global minimum and its applications to chaotic system identification and medical image processing. In: Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence; 1995 Nov 5-8; Japan. New York: IEEE; 1995. p.242-249
28. Lindner C, Wang C-W, Huang C-T, Li C-H, Chang S-W, Cootes TF. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Scientific Reports. 2016;6(1):1-7
29. Wang S, Li H, Li J, Zhang Y, Zou B. Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting. J Healthc Eng. 2018:1-15
30. Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 2017;4(1):1-12
31. Kim H, Shim E, Park J, Kim Y-J, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Computer Methods and Programs in Biomedicine. 2020;194:1-10
32. Lee JH, Yu HJ, Kim MJ, Kim JW, Choi J. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health. 2020;20(1):270
33. Lee M, Chung M, Shin YG. Cephalometric landmark detection via global and local encoders and patch-wise attentions. Neurocomputing. 2022;470:182-9
34. Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W. Automatic localization of cephalometric landmarks based on convolutional neural network. American Journal of Orthodontics and Dentofacial Orthopedics. 2022;161(3):250-9
35. Alessandri-Bonetti A, Sangalli L., Salerno M, Gallenzi P. Reliability of Artificial Intelligence-Assisted Cephalometric Analysis. A Pilot Study. BioMedInformatics. 2023;3(1),44-53
Published
2025-08-31
How to Cite
PRATIVI, Shinta Amini et al.
Development of cephalometric radiography in orthodontic imaging: a literature review.
Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI), [S.l.], v. 9, n. 2, p. 124-131, aug. 2025.
ISSN 2686-1321.
Available at: <http://jurnal.pdgi.or.id/index.php/jrdi/article/view/1344>. Date accessed: 29 nov. 2025.
doi: https://doi.org/10.32793/jrdi.v9i2.1344.
Section
Review Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
