Machine Learning‐Based Model for Predicting Short‐ and Long‐Term Growth in Untreated Class <scp>III</scp> Malocclusion

ABSTRACT Objective To develop a Machine Learning‐based model to predict growth of Caucasian subjects with untreated Class III malocclusion in the short‐ and long‐term. Materials and Methods A longitudinal sample of 144 Caucasian subjects with untreated Class III malocclusion was selected (80% training data; 20% test data). Cephalograms of the subjects of the test group were divided into short‐ and long‐term observations. Sixteen cephalometric landmarks were digitised in an X‐Y Cartesian coordinate system. The trained model was a Graph Neural Network. A one‐sample t ‐test and the Euclidean distances between predicted and observed values were calculated. Results In the short‐term prediction, 16 subjects were examined. On the X‐Y axis, the following cephalometric points were statistically significant: SX, PgY, BY, PNSY, NY and SY. Mean Euclidean distance between predicted and actual values revealed high values for the mandibular points Go (2.6 mm), Me (1.9 mm), Gn (1.9 mm), Pg (2.0 mm), and B (2.0 mm). In the long‐term prediction, 13 subjects were examined. On the X‐Y axis, the following cephalometric points were statistically significant: MeX, GnX, PgX, BX, BY and PtY. Mean Euclidean distance between predicted and actual values revealed high values for the mandibular points Go (3.1 mm), Me (4.3 mm), Gn (4.1 mm), Pg (4.5 mm), and B Point (4.1 mm). Conclusions The ML‐based prediction model was accurate for the majority of the landmarks. Cephalometric mandibular landmarks (Go, Me, Gn, Pg, and B Points) showed the highest mean Euclidean distances between predicted and observed values, indicating lower prediction accuracy.