This lecture will examine the efficacy of artificial intelligence algorithms in diagnosing and treating Class III orthodontic malocclusion. Specifically, the performance of statistical models will be compared to machine learning models trained on finished orthodontic cases to predict the most appropriate treatment approach for adult patients, including camouflage mechanotherapy and orthognathic surgery. Additionally, the potential utility of these models as adjunct features in imaging systems used in orthodontic practices will be discussed.
Learning Objectives:
After this lecture, attendees will be able to:
Understand the significance of proper diagnosis and treatment for Class III malocclusion, including the history and evolution of using expert systems and traditional statistics in orthodontic diagnosis and the most critical radiographic and clinical features that impact decision-making.
Compare the performance of machine learning models in predicting treatment plans for Class III malocclusion cases and examine their potential use in orthodontic practices.
Discuss strategies for improving diagnostic accuracy in Class III malocclusion cases using machine learning models.
