Cephalometric Landmark

Cephalometric landmark detection involves automatically identifying key anatomical points on lateral skull X-rays, crucial for orthodontic and orthognathic analysis. Current research heavily utilizes deep learning, employing various architectures like prototypical networks and multi-resolution fusion models to improve accuracy and address challenges like age-related variations in landmark appearance. These advancements aim to automate a time-consuming and potentially error-prone manual process, leading to more efficient and consistent cephalometric analysis in clinical practice and research. The development of large, publicly available datasets is also a significant focus, enabling the training and benchmarking of increasingly sophisticated algorithms.