Abstract: In the field of orthodontics, the accurate identification of cephalometric landmarks in dental radiography plays a crucial role in ensuring precise diagnoses and efficient treatment planning. Previous studies have demonstrated the impressive capabilities of advanced deep learning models in this particular domain. However, due to the ever-changing technological landscape, it is imperative to consistently investigate and explore emerging algorithms to further improve efficiency in this field. The present study centers around the assessment of the effectiveness of YOLOv8, the most recent version of the ’You Only Look Once (YOLO)’ algorithm series, with a particular emphasis on its autonomous capability to accurately identify cephalometric landmarks. In this study, a thorough examination was con-ducted to evaluate the YOLOv8 algorithm efficiency in detecting cephalometric landmarks. The assessments encompassed various aspects such as precision, adaptability in challenging conditions, and a comparative analysis with alternative algorithms. The predefined proximities of 2mm, 2.5mm, and 3mm were utilized for the comparisons. By focusing on its potential as a noteworthy breakthrough, the investigation seeks to ascertain whether the recent enhancements indeed bring about a significant stride in the precise identification of cephalometric landmarks.

Idriss Tafala, Fatima-Ezzahraa Ben-Bouazza, Aymane Edder, Oumaima Manchadi, Mehdi Et-Taoussi and Bassma Jioudi, “Cephalometric Landmarks Identification Through an Object Detection-based Deep Learning Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(2), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150286

@article{Tafala2024,
title = {Cephalometric Landmarks Identification Through an Object Detection-based Deep Learning Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150286},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150286},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {2},
author = {Idriss Tafala and Fatima-Ezzahraa Ben-Bouazza and Aymane Edder and Oumaima Manchadi and Mehdi Et-Taoussi and Bassma Jioudi}
}