Background: Sexual dimorphism in the facial skeleton is fundamental to aesthetics and gender perception. This study employs AI-assisted three-dimensional (3D) imaging to analyze morphological differences between males and females in the upper, mid, and lower facial regions. Methods: A retrospective analysis was conducted on 280 high-resolution craniofacial CT scans (147 females, 133 males) of adults aged 20 years and older. AI-assisted segmentation and modeling were performed using Materialise Mimics and 3-Matic software. The facial skeleton was divided into upper, mid, and lower regions for detailed morphometric evaluation. Statistical shape modeling (SSM) and heatmaps were used to visualize surface projection differences across age groups. Statistical comparisons were conducted to assess gender-based variation. Results: Significant sexual dimorphism was observed across all facial regions. In the upper face, males had greater interorbital width, frontotemporal brow width, inter-medial canthi distance, and more prominent frontal bossing. In the midface, males exhibited increased width, depth, and vertical height, with the most pronounced differences occurring in middle age. The lower face showed larger mandibular dimensions in males, including bigonial width, ramus height, and a sharper mental angle, while females had a more rounded chin and smoother mandibular contour. Heatmaps confirmed greater surface projection in male skeletons, particularly in the gonial and midfacial regions, with dimorphism decreasing with age. Conclusion: This represents the first large-sample CT study utilizing AI to comprehensively quantify facial skeletal dimorphism. Findings support the importance of gender-specific planning in facial reconstructive and aesthetic procedures.
Hussein et al. (2026) studied this question.