Development of a semi-automatic collaborative system for cephalometric measurement in orthodontic radiographic images
DOI:
https://doi.org/10.63688/cognitivatech.v1.i1.5Keywords:
Cephalometric analysis, Orthodontics, Semi-automatic system, Craniofacial measurement.Abstract
Cephalometric analysis is a fundamental tool in orthodontics for evaluating craniofacial morphology using radiographic images. However, traditional manual methods are time-consuming and show inter-observer variability, while fully automated artificial intelligence systems still face limitations in clinical reliability and generalization. This study presents a collaborative semi-automatic system designed to improve efficiency and consistency in two-dimensional cephalometric analysis. The system integrates digital templates with expert-assisted landmark identification, allowing trained users to mark anatomical reference points while the software automatically performs geometric calculations. A comparative evaluation was conducted between the manual and semi-automatic methods, focusing on processing time, landmark localization accuracy, and operational workload. The results showed a significant reduction in average processing time per radiograph, decreasing from 12.5 minutes using the manual method to 4.3 minutes with the proposed system. In addition, a decrease in variability in landmark identification was observed, particularly in mandibular structures where manual measurements tend to be less consistent. The semi-automatic approach also reduced workload by automating calculation and data recording processes while maintaining expert control during the marking phase. It is concluded that the combination of clinical expertise and computational support improves both efficiency and consistency in orthodontic analysis.References
Aboudara, C., Hatcher, D., Nielsen, I., & Miller, A. (2009). Evaluation of cone-beam computed tomography in orthodontics. American Journal of Orthodontics and Dentofacial Orthopedics. https://doi.org/10.1016/j.ajodo.2008.06.019 DOI: https://doi.org/10.1016/j.ajodo.2008.06.019
Alhazmi, A. (2020). Association between maxillary sinus dimensions and midface width: 2-D and 3-D CBCT study. Journal of Contemporary Dental Practice. https://doi.org/10.5005/jp-journals-10024-2756 DOI: https://doi.org/10.5005/jp-journals-10024-2725
Bajaj, K., Rathee, P., Jain, P., & Panwar, V. R. (2011). Reliability of anatomical landmarks in PA cephalometric radiographs and CT scans. Journal of Clinical and Diagnostic Research. https://doi.org/10.7860/JCDR/2011/1264.2130
Broadbent, B. H. (1931). A new X-ray technique and its application to orthodontia. Angle Orthodontist, 1, 45–66. https://doi.org/10.1043/0003-3219(1931)001%3C0045:ANXTAA%3E2.0.CO;2
Gribel, B. F., Gribel, M. N., Frazão, D. C., McNamara, J. A., & Manzi, F. R. (2011). Accuracy and reliability of craniometric measurements on CBCT scans. Angle Orthodontist, 81, 26–35. https://doi.org/10.2319/061310-333.1 DOI: https://doi.org/10.2319/032210-166.1
Hung, K., Montalvao, C., Tanaka, R., Kawai, T., & Bornstein, M. M. (2020). Artificial intelligence applications in dental and maxillofacial radiology: Systematic review. Dentomaxillofacial Radiology, 49. https://doi.org/10.1259/dmfr.20190107 DOI: https://doi.org/10.1259/dmfr.20190107
Kim, H., Shim, E., Park, J., Kim, Y. J., Lee, U., & Kim, Y. (2020). Web-based fully automated cephalometric analysis by deep learning. Computer Methods and Programs in Biomedicine, 194, 105513. https://doi.org/10.1016/j.cmpb.2020.105513 DOI: https://doi.org/10.1016/j.cmpb.2020.105513
Khalid, M. A., et al. (2022). CE-PhA29 landmark detection challenge. arXiv preprint. https://doi.org/10.48550/arXiv.2212.04808
Lim, K. F., et al. (2013). Reliability of cephalometric landmark identification. Angle Orthodontist. https://doi.org/10.2319/081212-635.1
Ludlow, J. B., et al. (2007). 3D imaging in orthodontics. American Journal of Orthodontics and Dentofacial Orthopedics. https://doi.org/10.1016/j.ajodo.2006.05.022 DOI: https://doi.org/10.1016/j.ajodo.2006.05.022
Mosleh, M. A. A., Baba, M. S., Malek, S., & Almaktari, R. A. (2016). Ceph-X: Development and evaluation of a 2D cephalometric system. BMC Bioinformatics, 17, 499. https://doi.org/10.1186/s12859-016-1329-4 DOI: https://doi.org/10.1186/s12859-016-1370-5
Pittayapat, P., et al. (2013). CBCT in orthodontic diagnosis. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. https://doi.org/10.1016/j.oooo.2013.05.015 DOI: https://doi.org/10.1016/j.oooo.2013.05.015
Pittayapat, P., et al. (2014). Three-dimensional cephalometric analysis: Systematic review. Orthodontics & Craniofacial Research. https://doi.org/10.1111/ocr.12034 DOI: https://doi.org/10.1111/ocr.12034
Płotka, S., et al. (2021). Convolutional neural networks in orthodontics: A review. arXiv preprint. https://doi.org/10.48550/arXiv.2104.08886
Raj, G., et al. (2023). Accuracy of CBCT-synthesized cephalograms. Journal of Dental Research. https://doi.org/10.1177/14653125231178038
Sarwar, S., & Jabin, S. (2023). Artificial intelligence in dental CBCT imaging. arXiv preprint. https://doi.org/10.48550/arXiv.2306.03025
Smołka, P., et al. (2024). AI vs orthodontists in cephalometric analysis. Applied Sciences, 14(12), 4972. https://doi.org/10.3390/app14124972 DOI: https://doi.org/10.3390/app14124972
Urooge, A., & Patil, B. A. (2017). Sexual dimorphism of maxillary sinus using CBCT. Journal of Clinical and Diagnostic Research, 11, ZC67–ZC70. https://doi.org/10.7860/JCDR/2017/27067.10429
Yamaguchi, T., Maki, K., & Shibasaki, Y. (2001). Growth hormone receptor gene variant and mandibular height. American Journal of Orthodontics and Dentofacial Orthopedics, 119, 650–653. https://doi.org/10.1067/mod.2001.117787 DOI: https://doi.org/10.1067/mod.2001.114536
Huang, Y., et al. (2020). Cephalogram synthesis and landmark detection in CBCT. arXiv preprint. https://doi.org/10.48550/arXiv.2009.04420
Raj, G., et al. (2024). CBCT vs conventional cephalograms accuracy comparison. International Journal of Oral and Maxillofacial Surgery. https://doi.org/10.1177/14653125231178038 DOI: https://doi.org/10.1177/14653125231178038
Published
Issue
Section
License
Copyright (c) 2024 Alfredo Stefano Alvarado Sánchez (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
