O.259 3D-planning and navigation in CMF surgery

Abstract

Landmark identification is crucial in quantifying cephalometric analysis. Manual tracing of landmarks is a tedious, laborious task and prone to human errors, thereby, necessitating a need to develop efficient automated methods for landmark identification. The advent of artificial intelligence and machine learning has made the automation of cephalometric landmarking, seemingly possible. Techniques such as active shape modelling, active appearance modelling, random forest regression-voting, Convolutional Neural Network (CNN), fuzzy systems and many others have rendered promising results in this field. This work reviews and critically analyses various techniques used for cephalometric landmark identification. This study also highlights current applications, addresses gaps in literature and presents the open challenges in this field.