Predicting camouflage treatment outcomes in skeletal class III malocclusion using machine learning

This study focused on developing a machine learning (ML) model to forecast the success of camouflage orthodontic treatment in individuals with skeletal Class III malocclusion and to identify significant predictors to aid treatment planning. A total of 100 adult patients who had skeletal Class III malocclusion and were treated with camouflage orthodontics were analyzed retrospectively. Treatment success was defined by an overjet exceeding 2 mm, proper canine relationship, and appropriate molar relationship (as applicable). Four machine learning algorithms (Random Forest, CART, Neural Network, and XGBoost) were trained and evaluated using fivefold cross-validation. Cephalometric variables were analyzed before and after treatment, and model performance was evaluated. Among all metrics, XGBoost exhibited the best predictive performance, suggesting better generalization. A decision tree model showed that the sagittal position of the lower incisors (L1_x) and palatal length (Palatal L) were the most influential predictors. An L1_x of less than 76 mm and a Palatal L of 41 mm or greater were strongly associated with successful treatment. ML algorithms, particularly XGBoost, can forecast the effectiveness of camouflage treatment for skeletal Class III malocclusion. Key predictors can guide treatment planning and support artificial intelligence-assisted orthodontic decisions.