COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS

The pre-adolescent growth period is the best time for the skeletal Class-III malocclusion treatment. Diagnosis and treatment during this period continue to be a complex orthodontic problem. Class-III malocclusion is complicated to treat with braces frequently requiring surgical intervention after a pubertal growth spurt. In addition, delayed recognition of the problem will yield significant functional, aesthetic, and psychological concerns. This study presents the first fully automated machine learning method to accurately diagnose Class-III malocclusion applied across mobile images, to the best of our knowledge. For this purpose, we comparatively evaluated three machine learning approaches: a deep learning algorithm, a machine learning algorithm, and a rule-based algorithm. We collected a novel profile image data set for this analysis along with their formal diagnosis from 435 orthodontics patients. The most successful method among the three was the machine learning method, with an accuracy of %76.