Virtual surgical planning-to-actual surgical outcome discrepancies following artificial intelligence-assisted and manual three-dimensional cephalometric tracing in orthognathic surgical planning

This study compared discrepancies between virtual surgical planning (VSP) and actual surgical outcomes (ASO), as well as time efficiency, among manual and AI-assisted three-dimensional (3D) cephalometric tracing workflows in OGS planning. Fifteen patients undergoing bimaxillary orthognathic surgery were retrospectively reviewed. Three preoperative virtual surgical plans were created for each patient: manual tracing in Invivo, manual tracing in ON3D, and AI-assisted tracing in ON3D. The primary comparison was conducted between the manual and AI-assisted ON3D workflows, with Invivo included as a platform-level reference workflow. The primary endpoint was the 3D discrepancy between VSP and ASO at eight skeletal and dental landmarks along the X-, Y-, and Z-axes. Manual and AI-assisted ON3D workflows showed no statistically significant differences in VSP-to-ASO discrepancies for most skeletal landmarks along the X- and Y-axes, with absolute mean differences generally within clinically acceptable limits. Both ON3D workflows showed statistically significant Z-axis discrepancies at mandibular landmarks, including Point B, Pogonion, and Menton; however, several were approximately 1–1.5 mm and were not specific to AI-assisted tracing. Maxillary landmark discrepancies were generally within ± 1 mm across ON3D workflows and remained within clinically acceptable limits. AI-assisted tracing reduced mean preoperative construction time from approximately 10.5 min to 4.2 min and reduced total planning time from approximately 17–18 min to 7–9 min. Inter-examiner reliability for time measurements was high, with ICC values exceeding 0.75. In contrast, inter-examiner reliability for ASO landmark identification was only fair-to-moderate (ICC = 0.47). AI-assisted 3D cephalometric tracing showed no statistically significant differences in VSP-to-ASO discrepancies compared with manual tracing workflows, while improving planning efficiency. However, these findings should be interpreted as downstream workflow-based outcome data rather than direct validation of coordinate-level AI landmark identification accuracy.