Contemporary advances in automated landmark detection in cephalometrics

Contemporary methods for automatic landmark detection in cephalometrics are based on the results and positive experiences of traditional techniques such as edge detection or AAM and utilize cutting-edge technologies, including deep learning and machine learning algorithms. These advanced approaches harness convolutional neural networks and other sophisticated models to achieve robust and accurate identification of anatomical landmarks on cephalometric images. These methods transcend traditional limitations, accommodating variations in anatomy and enhancing precision. The synergy of these contemporary methods not only revolutionizes orthodontic practices but also contributes to interdisciplinary research, propelling the field towards a future of enhanced diagnostics and treatment planning.