Deep Learning-Assisted 3D Analysis of Coronoid Process Changes After Orthognathic Surgery

Background/Objectives: Postoperative remodeling and positional deviations of the mandibular coronoid process (CP) after orthognathic surgery remain insufficiently characterized, particularly in three-dimensional analyses. The aim of this study was to evaluate qualitative and quantitative CP changes following bimaxillary orthognathic surgery using a deep learning-assisted three-dimensional workflow. Methods: This retrospective study included 75 patients treated with combined orthodontic–surgical therapy, including 25 patients with skeletal Class II malocclusion and 50 patients with skeletal Class III malocclusion. Preoperative and 6-month postoperative computed tomography scans were analyzed. Automatic segmentation and three-dimensional reconstruction were performed using a convolutional neural network based on the nnU-Net architecture. Qualitative assessment included evaluation of CP displacement patterns and visualization of local surface differences using heat maps. Quantitative analysis included volumetric assessment of preoperative and postoperative CP models, calculation of apposition-compatible (Vapo) and resorption-compatible (Vres) volumetric changes, and mixed-effects modeling accounting for within-patient correlations. Results: Medial displacement of the CP predominated in both skeletal classes and was more frequent in Class III patients. Qualitative surface analysis demonstrated a consistent location-dependent remodeling pattern, characterized by predominant apposition-compatible changes on the lateral and medial surfaces and predominant resorption-compatible changes along the anterior border. Quantitative analyses revealed an overall positive remodeling balance, although substantial inter-individual variability was observed. Mixed-effects analyses demonstrated no significant overall effects of side or skeletal class on volumetric remodeling; however, a significant interaction between side and skeletal class was identified for net remodeling balance. A significant random patient effect indicated considerable variability in remodeling response among individuals. Conclusions: AI-assisted three-dimensional analysis enables a reproducible assessment of postoperative CP remodeling following orthognathic surgery. Coronoid process remodeling is characterized by heterogeneous, location-dependent surface changes and substantial inter-individual variability. The observed remodeling patterns are compatible with adaptive responses to altered postoperative biomechanical conditions, although the underlying biological mechanisms remain to be clarified.