Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal...

Area of Science:

• Craniofacial surgery
• Medical imaging
• Anthropometry

Background:

• Facial skeletal dimorphism is crucial in gender-affirming surgery.
• Existing cephalometric studies lack detailed skeletal contour insights for surgical planning.
• Statistical shape modeling (SSM) is proposed as a novel tool for analyzing mandibular dimorphism.

Purpose of the Study:

• To investigate mandibular dimorphism in young white individuals using SSM.
• To provide detailed skeletal contour insights for surgical planning in gender-affirming procedures.
• To establish a foundation for individualized operative planning using SSM.

Main Methods:

• Retrospective CT scan analysis of 98 white individuals (20-39 years old).
• AI-assisted 3D mandible reconstruction and SSM for average gender models.
• 3D overlays and heatmaps to visualize contour disparities, validated by anthropometric measurements.

Main Results:

• Male mandibles exhibited greater bigonial width, intercondylar width, ramus height, and body length (p<0.005).
• No significant difference in gonial angle (p=0.62).
• SSM 3D overlays showed squarer male chins with more lateral, less anterior projection; smoother female gonial angle transitions.

Conclusions:

• SSM objectively quantifies volumetric and contour dimorphisms between genders.
• The automated SSM method expedites comparative population analyses.
• This approach advances anthropometric assessment for aesthetic and gender-affirming facial surgery.