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

In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals.

A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment.

Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle.

SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment.

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.