References
- Broadbent BH. A new X-ray technique and its application to orthodontia. Angle Orthodontist. 1931; 1:45–66.
- Hofrath H. Die bedeutung der roentgenfern der kiefer anomalien. Fortschr Orthodontic. 1931; 1:232–48.
- Baumrind S, Frantz RC. The reliability of head film measurements 1 Landmark identification. Am J Orthod. 1971; 60:111–27.
- Baumrind S, Frantz RC. The reliability of head film measurements 2. Conventional angular and linear measures. Am J Orthod. 1971; 60:505–17.
- Wang C-W, Huang C-T, Hsieh M-C, Li C-H, Chang S-W, Li W-C, et al. Evaluation and comparison of anatomical landmark detection methods for cephalometric X-Ray Images. IEEE Trans Med Imaging. 2015; 34:1890–900.
- Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—a review. Front Dental Med. 2023; 4:1–13.
- Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, et al. Automatic localization of cephalometric landmarks based on convolutional neural network. Am J Orthod Dentofacial Orthop. 2022; 161: 250–9.
- Cohen J. Statistical power analysis for the behavioral sciences. (2nd ed.). Hillsdale, NJ: Erlbaum; 1988.
- Duran GS,Gökmen Ş, Topsakal KG, Görgülü S. Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm. Orthod Craniofacial Res. 2023; 26:481–90.
- Hassan MM, Hadi Alfaifi WH, Qaysi AM, Alfaifi AA, AlGhafli ZM, Mattoo KA, et al. Comparative evaluation of digital cephalometric tracing applications on mobile devices and manual tracing. Med Sci Monitor. 2024; 30:e944628.
- Alessandri-Bonetti A, Sangalli L, Salerno M, Gallenzi P. Reliability of artificial intelligence-assisted cephalometric analysis. A pilot study. BioMedInformatics. 2023; 3:44–53.
- Mercier J-P, Rossi C, Sanchez IN, Renovales ID, Sahagún PM-P, Templier L. Reliability and accuracy of Artificial intelligence-based software for cephalometric analysis. BMC Oral Health. 2024; 24:1309.
- Ristau B, Coreil M, Chapple A, Armbruster P, Ballard R. Comparison of AudaxCeph®‘s fully automated cephalometric tracing technology to a semi-automated approach by human examiners. Int Orthod. 2022; 20:100691.
- Tanikawa C, Lee C, Lim J, Oka A, Yamashiro T. Clinical applicability of automated cephalometric landmark identification: Part I-Patient-related identification errors. Orthod Craniofacial Res. 2021; 24(Suppl 2):43–52.
- Le VNT, Kang J, Oh I-S, Kim J-G, Yang Y-M, Lee D-W. Effectiveness of human-artificial intelligence collaboration in cephalometric landmark detection. J Personalized Med. 2022; 12:387.
- Kim YH, Lee C, Ha E-G, Choi YJ, Han S-S. A fully deep learning model for the automatic identification of cephalometric landmarks. Imaging Sci Dentistry. 2021; 51:299–306.
- 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; 22:132.
- Bulatova G, Kusnoto B, Grace V, Tsay TP, Avenetti DM, Sanchez FJC. Assessment of automatic cephalometric landmark identification using artificial intelligence. Orthod Craniofacial Res. 2021; 24(Suppl 2):37–42.