Artificial Intelligence-Based McNamara Analysis of Different Types of Cleft and Non-Cleft Individuals

Introduction: This research utilizes artificial intelligence-based McNamara cephalometric analysis to evaluate skeletal discrepancies in individuals with and without cleft lip and palate (CLP). The study examines gender differences and compares McNamara parameters across cleft types using a comprehensive dataset. Methods: Demographic data and McNamara measurements, including maxillary length (Co-A), mandibular length (Co-Gn) and maxillomandibular differential (MMD), were extracted from 123 individuals. Statistical analyses included independent samples tests, ANOVA, robust testing, and Tukey HSD post-hoc comparisons. Results: The sample comprised 56.9% males and 43.1% females. Cleft distribution: non-cleft (NC) (24.4%), bilateral cleft lip and palate (BCLP) (43.9%), unilateral cleft lip and palate (UCLP) (13.8%), unilateral cleft lip (UCL) (10.6%), unilateral cleft lip and alveolus (UCLA) (7.3%). No significant gender differences were observed in McNamara parameters. Significant differences existed between NC and CLP groups in all parameters (p < 0.001). ANOVA revealed significant differences across cleft types for Co-A (p = 0.002), Co-Gn (p = 0.003) and MMD (p < 0.001). Post-hoc tests identified specific group differences. Conclusion: While gender does not significantly impact McNamara parameters, significant differences exist between NC and CLP individuals and among cleft subtypes. These findings highlight the importance of cleft-specific considerations in orthodontic and craniofacial treatment planning.