No abstract

Among skeletal deformities, Class III is the one that best time for the treatment is the pre-adolescent growth period. Diagnosis and treatment in this period continue to be a complex orthodontic problem. Class III malocclusion is especially difficult to treat with braces frequently requiring surgical intervention after pubertal growth spurt. In addition, delayed recognition of the problem will yield to significant functional, aesthetic and psychological concerns. In this study, we proposed a comparative analysis of three predictive models to predict Class III malocclusion: deep learning algorithm, machine learning algorithm and a rule-based algorithm. For this analysis, we collected a novel profile image data set 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.

Among skeletal deformities, Class III is the one that best time for the treatment is the pre-adolescent growth period. Diagnosis and treatment in this period continue to be a complex orthodontic problem. Class III malocclusion is especially difficult to treat with braces frequently requiring surgical intervention after pubertal growth spurt. In addition, delayed recognition of the problem will yield to significant functional, aesthetic and psychological concerns. In this study, we proposed a comparative analysis of three predictive models to predict Class III malocclusion: deep learning algorithm, machine learning algorithm and a rule-based algorithm. For this analysis, we collected a novel profile image data set 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.