Deep Learning in Cephalometric Analysis: A Scoping Review of Automated Landmark Detection

Cephalometric landmark identification is funda-mental for accurate cephalometric analysis, serving as a corner-stone in orthodontic diagnosis and treatment planning. However, manual tracing is a labor-intensive process prone to inter-observer variability and human error, highlighting the need for automated methods to improve precision and efficiency. Recent advances in Deep Learning have enabled automatic detection of cephalometric landmarks, thereby increasing accuracy and consistency while reducing processing time. This scoping review examines contemporary applications of Deep Learning in cephalometric landmark detection and cephalometric analysis from 2019 to January 2025. We searched IEEEXplore, Sci-enceDirect, arXiv, Springer, and PubMed databases, identifying 601 articles, of which 76 met inclusion criteria after rigorous screening. Our analysis revealed significant performance improvements with Deep Learning methods achieving Success Detection Rates (SDR) of 75-90% at 2mm thresholds, substantially out-performing traditional methods. Geographical analysis identified China, South Korea, and the United States as leading research centers, with commercial applications like WebCeph and CephX gaining clinical adoption. Deep Learning improves the accuracy and efficiency of cephalometric analysis; however, challenges persist regarding dataset standardization and clinical validation. These technologies show promising potential to support novice clinicians, streamline radiological examinations, and improve landmark identification reliability in routine orthodontic practice.

@article{Tafala2025,
  title     = {Deep Learning in Cephalometric Analysis: A Scoping Review of Automated Landmark Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Idriss Tafala and Fatima-Ezzahraa Ben-Bouazza and Aymane Edder and Oumaima Manchadi and Bassma Jioudi},
  doi       = {10.14569/IJACSA.2025.0160692},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160692}
}