Explainable CBCT based morphometric clustering for orthodontic diagnostics using principal component analysis and k means

Background : Cone‑beam computed tomography (CBCT) enables three‑dimensional assessment of craniofacial structures, yet interpretation of many inter‑correlated measurements remains challenging. Objective : To present an explainable workflow combining principal component analysis (PCA) and k‑means clustering to identify skeletal Class II phenotypes. Methods : Sixty‑three CBCT‑derived variables from 120 Yemeni adults with skeletal Class II malocclusion were standardized, reduced via PCA, and clustered (k = 2–8). Internal validity was evaluated using silhouette and Davies–Bouldin indices with multiple random initializations. Results : Seven principal components explained approximately 60% of variance; a five‑cluster solution balanced cohesion, separation, and clinical interpretability, delineating deep‑bite and open‑bite tendencies, mandibular retrusion patterns, and incisor‑protrusive phenotypes. Conclusions : The PCA→k‑means pipeline provides a transparent, reproducible framework for artificial intelligence (AI)-assisted orthodontic diagnosis and phenotype‑informed treatment planning. Trial registration: Not applicable (retrospective observational study; no prospective assignment to interventions).