Early Prediction of the Need for Orthognathic Surgery in Patients With Repaired Unilateral Cleft Lip and Palate Using Machine Learning and Longitudinal Lateral Cephalometric Analysis Data.

The purpose of this study was to determine the cephalometric predictors of the future need for orthognathic surgery in patients with repaired unilateral cleft lip and palate (UCLP) using machine learning. This study included 56 Korean patients with UCLP, who were treated by a single surgeon and a single orthodontist with the same treatment protocol. Lateral cephalograms were obtained before the commencement of orthodontic/orthopedic treatment (T0; mean age, 6.3 years) and at at least of 15 years of age (T1; mean age, 16.7 years). 38 cephalometric variables were measured. At T1 stage, 3 cephalometric criteria (ANB ≤ -3°; Wits appraisal ≤ -5 mm; Harvold unit difference ≥34 mm for surgery group) were used to classify the subjects into the surgery group (n = 10, 17.9%) and non-surgery group (n = 46, 82.1%). Independent t-test was used for statistical analyses. The Boruta method and XGBoost algorithm were used to determine the cephalometric variables for the prediction model. At T0 stage, 2 variables exhibited a significant intergroup difference (ANB and facial convexity angle [FCA], all P < 0.05). However, 18 cephalometric variables at the T1 stage and 14 variables in the amount of change (ΔT1-T0) exhibited significant intergroup differences (all, more significant than P < 0.05). At T0 stage, the ANB, PP-FH, combination factor, and FCA were selected as predictive parameters with a cross-validation accuracy of 87.4%. It was possible to predict the future need for surgery to correct sagittal skeletal discrepancy in UCLP patients at the age of 6 years.