Can Childhood Longitudinal Cephalograms Predict Adult Facial Soft-Tissue Dimensions? A Bayesian Modelling Study.

INTRODUCTION: Predicting facial soft-tissue growth is essential for treatment planning and long-term outcome forecasting in orthodontics, orthognathic surgery and facial aesthetic surgery. This study evaluated whether longitudinal childhood cephalograms can predict adult facial soft-tissue dimensions using Bayesian growth models. METHODS: Serial lateral cephalograms from four studies in the AAOF Craniofacial Growth Legacy Collection (Bolton-Brush, Denver, Iowa, Oregon) were analysed. The sample comprised 96 subjects (39 females, 57 males), over an age range of 6-29 years. Eight soft-tissue linear measurements were analysed: upper and lower facial heights, upper and lower lip lengths, upper and lower lip thicknesses and soft-tissue chin thickness at pogonion and menton. Population-level and individual-level Bayesian models were fitted for each variable. Predictive accuracy was assessed using five-fold cross-validation, with a childhood cutoff age of 14 years and prediction of withheld adult observations at age > 20 years. Predictive accuracy was quantified using the mean absolute error, bias and 95% prediction interval coverage. RESULTS: Mean absolute error ranged from 0.73 mm (lower lip thickness) to 2.85 mm (lower facial height), with 95% prediction interval coverage of 94.1%-99.0%. Bias was close to zero for all variables. Residual standard deviation reduction from the individual-level model ranged from 3% (upper lip length) to 42% (lower lip length). Prediction accuracy was generally higher in females than males. CONCLUSION: Longitudinal childhood cephalometric records contain predictive information regarding adult facial soft-tissue dimensions beyond population-based growth standards. Prediction accuracy and inter-individual variation both differed by soft-tissue variable and sex.