Cephalometric Analysis

Cephalometric analysis involves identifying key anatomical landmarks on medical images (primarily X-rays and 3D photographs) of the head and face, crucial for orthodontic diagnosis and treatment planning. Current research heavily utilizes deep learning, particularly convolutional neural networks like U-Net and DiffusionNet, to automate landmark detection, improving accuracy and efficiency compared to manual methods. This automation is significantly advancing the field by enabling faster, more consistent analysis of large datasets, ultimately leading to improved diagnostic precision and personalized treatment strategies in orthodontics.