El Merouani Drissi A, Ben Mohimd H, Alassfar A, Ahmadoun D, Zaoui F, Benyahia H. Evaluation of Accuracy and Reproducibility of an AI-Based Cephalometric Landmark Detection Software: A Comparative Study with Manual Annotations. J Clin Exp Dent. 2026;18(6):e720-8.

 

doi:10.4317/jced.64030

https://doi.org/10.4317/jced.64030

___

 

References

1. Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham). 2017;4(1):014501.
https://doi.org/10.1117/1.JMI.4.1.014501
PMid:28097213 PMCid:PMC5220585

 

2. Kim IH, Kim Y, Han S, Park JW, Kim N. Comparing intra-observer variation and automated cephalometric landmark identification on lateral cephalograms. Sci Rep. 2021.

 

3. 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):903-909.
https://doi.org/10.2319/022019-127.1
PMid:31282738 PMCid:PMC8109157

 

4. Polizzi A, Leonardi R. Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews. J Dent. 2024;146:105056.
https://doi.org/10.1016/j.jdent.2024.105056
PMid:38729291

 

5. Kim YH, Lee C, Ha EG, Choi YJ, Han SS. A fully deep learning model for the automatic identification of cephalometric landmarks. Imaging Sci Dent. 2021;51(3):299-306.
https://doi.org/10.5624/isd.20210077
PMid:34621657 PMCid:PMC8479429

 

6. 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.
https://doi.org/10.1007/s00784-021-03990-w
PMid:34046742 PMCid:PMC8310492

 

7. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol. 2020;49(1):20190107.
https://doi.org/10.1259/dmfr.20190107
PMid:31386555 PMCid:PMC6957072

 

8. 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 May 1;91(3):329-335.
https://doi.org/10.2319/021220-100.1
PMid:33434275 PMCid:PMC8084461

 

9. 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.
https://doi.org/10.1016/j.jtumed.2021.08.010
PMid:35140566 PMCid:PMC8801471

 

10. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-4.
https://doi.org/10.1001/jama.2013.281053
PMid:24141714

 

11. Durão AR, Pittayapat P, Rockenbach MI, Olszewski R, Ng S, Ferreira AP, et al. Validity of 2D lateral cephalometry in orthodontics: a systematic review. Prog Orthod. 2013;14(1):31.
https://doi.org/10.1186/2196-1042-14-31
PMid:24325757 PMCid:PMC3882109

 

12. Houston WJ. The analysis of errors in orthodontic measurements. Am J Orthod. 1983;83(5):382-90.
https://doi.org/10.1016/0002-9416(83)90322-6
PMid:6573846

 

13. McClure SR, Sadowsky PL, Ferreira A, Jacobson A. Reliability of digital versus conventional cephalometric radiology: a comparative evaluation. American Journal of Orthodontics and Dentofacial Orthopedics, 2005;128(6):721-726.

 

14. Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513.
https://doi.org/10.1016/j.cmpb.2020.105513
PMid:32403052

 

15. Yang S, Song ES, Lee ES, Kang SR, Yi WJ, Lee SP. Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network. BMC Oral Health. 2023 Oct 27;23(1):803. Erratum in: BMC Oral Health. 2024 Feb 1;24(1):165.
https://doi.org/10.1186/s12903-023-03804-3
PMid:38302984 PMCid:PMC10835831

 

16. Midtgård J, Björk G, Linder-Aronson S. Reproducibility of cephalometric landmarks and errors of measurements of cephalometric cranial distances. Angle Orthod. 1974;44(1):56-61.

 

17. Stabrun AE, Danielsen K. Precision in cephalometric landmark identification. Eur J Orthod. 1982;4(3):185-96.
https://doi.org/10.1093/ejo/4.3.185
PMid:6957301

 

18. Trpkova B, Major P, Prasad N, Nebbe B. Cephalometric landmarks identification and reproducibility: a meta analysis. Am J Orthod Dentofacial Orthop. 1997;112(2):165-70.
https://doi.org/10.1016/S0889-5406(97)70242-7
PMid:9267228

 

19. Rudolph DJ, Sinclair PM, Coggins JM. Automatic computerized radiographic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop. 1998;113(2):173-9.
https://doi.org/10.1016/S0889-5406(98)70289-6
PMid:9484208

 

20. Panesar S, Manoharan G, Alshayji AA, et al. Precision and accuracy assessment of cephalometric analyses performed by deep-learning artificial intelligence. Applied Sciences. 2023;13.
https://doi.org/10.3390/app13126921

 

21. Londoño J, Aboal DG, Peña CG. Accuracy of deep learning models for cephalometric landmark detection: a systematic review and meta-analysis. Dentomaxillofacial Radiology. 2023;52(9):20230073.

 

22. Bao P, Lee JS, Kim S. Evaluation of cephalometric landmark detection methods: clinical acceptability thresholds. Journal of Dental Research. 2022;101(12):1426.

 

23. Buscemi N, Hartling L, Vandermeer B, Tjosvold L, Klassen TP. Single data extraction generated more errors than double data extraction in systematic reviews. J Clin Epidemiol. 2006;59(7):697-703.
https://doi.org/10.1016/j.jclinepi.2005.11.010
PMid:16765272