Accuracy of machine learning-assisted prediction of the future need for orthognathic surgery in patients with cleft lip and palate - Lim, Seung-Weon; Kim, Eunghee; Kim, Hong-Gee; Baek, Seung-Hak (Korean journal of orthodontics (2012), 2025)
To investigate the accuracy of machine learning (ML)-assisted prediction of the need for orthognathic surgery (OGS) in patients with cleft lip and palate (CLP). This study included 245 patients with CLP whose lateral cephalograms were available at pre-adolescence (T0; mean age, 8.45 years) and young adulthood (T1; mean age: 18.37 years). At T1, the patients were classified into the surgery group based on two criteria: (1) satisfying at least three of the following four conditions: ANB < -3°, Wits appraisal < -5 mm, APDI > 90°, and AB-MP < 60° and (2) undergoing presurgical orthodontic treatment or having undergone OGS. A total of 25.3% (n = 62) of patients were assigned to the surgery group, while 74.7% (n = 183) were assigned to the non-surgery group. Further, 80% and 20% of each group were used as training/validation and test sets, respectively. After 37 cephalometric variables and two cleft-related variables were measured, support vector machine (SVM) and feature importance analysis (FIA) with Shapley additive explanation were used to determine the prediction accuracy and predictors at T0. SVM demonstrated area under curve 0.84, accuracy 83.7%, sensitivity 83.3%, and specificity 83.8%. FIA revealed 10 predictors: A to N-perpendicular, L1 to A-Pog, Pog to N-perpendicular, L1 to Lower-occlusal plane, Cleft type, U1 to Upper-occlusal plane, IMPA, gonial angle, anteroposterior facial height ratio, and ANB with accumulated importance of 64.51%. The ML algorithm used in this study may support clinical decision-making in identifying candidates for future OGS at 8 years of age.