Learning Orthodontic Cephalometry through Augmented Reality: A Conceptual Machine Learning Validation Approach

摘要

The orthodontic cephalometry which forms a quintessential radiographic examination provides a valuable diagnostic resource for everyday orthodontic diagnosis and treatment planning. The method of analyzing the lateral cephalometric radiograph (LCR) has been found to lack effective methods of landmark identification and objective validation. This drawback, with the use of augmented reality (AR) for 3D visualization and machine learning for objective validation, can be effectively incorporated into the learning of cephalometrics. The two technologies together can produce a learning platform by reducing subjective errors and its implications on treatment planning in addition to effectively eliminating dissonance in learning.