Dento-skeletal Class III malocclusion in growing patients is one of the most challenging problems in orthodontics due to the difficulty in maintaining long-term stability. Ideally, a better diagnosis and prognosis are required to set up an individualized treatment plan. The integration of Artificial Intelligence with related machine learning algorithms seems to offer a solution for evaluating the kind of growth and improving the prediction of therapeutic success. In the meantime, an early therapeutic approach has been widely used. The rationale has been to intervene for aesthetic and psychological reasons while decreasing the severity of the adverse Class III dento-skeletal relationship after growth is completed. Many early Class III treatment alternatives have been developed, although long-term data are still few. Thus, this presentation will focus on short- and long-term outcomes in hypo- and hyper-divergent children treated with the SEC III protocols in comparison to controls.
Learning Objectives:
After this session, attendees will be able to:
Understand the rationale for early treatment in growing patients with Class III malocclusion.
Assess current perspectives and evidence-based information in the prognosis of early treatment outcomes in patients with Class III malocclusion.
Evaluate the short- and long-term stability of SEC III protocols in hypo- and hyper-divergent Class III malocclusion compared to control groups.
