CBCT-Based Three-Dimensional Phenotyping of Skeletal Class II Malocclusion in Yemeni Adults: A Multivariate Workflow for AI-Ready Orthodontic Diagnostics

Background: Skeletal Class II malocclusion is among the most common orthodontic problems, showing substantial variation in its craniofacial presentation. Objective: To identify distinct three-dimensional craniofacial phenotypes of Class II malocclusion in a Yemeni adult population using CBCT and multivariate analysis. Methods: CBCT scans from Yemeni adults were analyzed. Linear and angular cephalometric parameters were extracted. Principal component analysis (PCA) and cluster analysis (CA) were applied to derive phenotypes. Results: Five distinct phenotypes were identified, reflecting variability in sagittal, vertical, and transverse skeletal parameters. Canonical discriminant analysis confirmed robust separation. Conclusion: CBCT-based phenotyping highlights the heterogeneity of Class II malocclusion. Findings can guide individualized treatment planning and provide a framework for integrating AI and genetics into orthodontic diagnostics.