EfficientNetV2 and Attention Mechanisms for the automated detection of Cephalometric landmarks

Artificial intelligence (AI) is becoming acknowledged for its significant potential to bring about transformation across various sectors, including dentistry. Cephalometry is crucial in craniofacial imaging for medical diagnostics, assisting in diagnosis, surgical planning, and growth research. Manual cephalometric analysis is laborious and error-prone, emphasizing the necessity for automated landmark identification techniques to improve efficiency and precision. This paper proposes a new method for completely automating the localization of cephalometric landmarks using the deep neural network EfficientNetV2 with attention mechanisms. The goal of our approach is to precisely identify specific points on lateral cephalograms, overcoming the challenge of having a small amount of data in the medical field. This research enhances diagnostic accuracy and treatment planning in dentistry by improving automated landmark detection in cephalometry, leading to better patient care and outcomes. By integrating cutting-edge deep learning models with craniofacial imaging, this research aligns with the broader goal of leveraging AI to improve patient care across various medical fields.