Three Data‐Driven Lip Soft‐Tissue Phenotypes and Their Dento‐Skeletal Associations in Orthodontic Patients: An Exploratory Unsupervised Learning Study

BACKGROUND: Conventional facial diagnosis emphasizes skeletal and dental measures while lip features are assessed piecemeal. We aimed to identify data-driven lip soft-tissue phenotypes using unsupervised machine learning and examine their associations with skeletal and dental characteristics. METHODS: In a retrospective cross-sectional study, 10 lip measurements were extracted from standardized lateral cephalometric radiographs. Variables were z-score standardized and analysed using k-means clustering. Of 26 NbClust validation indices, 14 (53.8%) supported a three-cluster solution. Cluster stability was assessed by bootstrap resampling (B = 100) and 10-fold cross-validation. Post hoc feature importance was evaluated using PCA, Random Forest, SVM and XGBoost. A decision tree was trained and evaluated by 10-fold cross-validation repeated five times. Associations with craniofacial parameters were examined via within-class ANOVA, Cramér's V and hierarchical regression. Concordant pairings were defined as Cluster 1-Class I, Cluster 2-Class II and Cluster 3-Class III; all others were classified as discordant. RESULTS: adj = 0.058-0.078, p < 0.001). An interpretable decision tree achieved 88.0% cross-validated accuracy (macro-F1 = 0.876) for phenotype assignment. CONCLUSIONS: Unsupervised learning identified three reproducible lip soft-tissue phenotypes that complement conventional dentoskeletal description by capturing within-class soft-tissue heterogeneity. The framework is exploratory and requires prospective external validation and outcome-based studies before clinical triage application.