How Does the Mandible Age? Comprehensive Artificial Intelligence-Assisted Shape Analysis in the White Population

Abstract

Background: Mandible contour significantly influences facial appearance, framing the lower facial silhouette. Redefining mandibular contour is key for facial and neck rejuvenation. Yet, there is limited facial aging research across different lifespans and sexes. Here, we utilize artificial intelligence and advanced 3-dimensional (3D) analysis to elucidate mandibular aging patterns in male and female subjects.

Methods: A retrospective analysis of facial computed tomography scans in White patients was conducted, categorizing subjects into 3 age groups (20-79 y) and stratifying them by sex. Artificial intelligence-assisted segmentation into 3D mandibles was done in Mimics v.25, and statistical shape modeling was used to create an average mandible for each group. Volume and linear measurements were assessed via 3D overlays.

Results: Analysis of 280 mandibles demonstrated statistically significant aging changes in both sexes. Ramus height showed a marked decrease with age, by approximately 5.3 mm in women and 4.2 mm in men (P < 0.001). Interrami and intercondylar widths increased by a mean of 4-5 mm (P < 0.01). Women exhibited an increase in mandibular angle (P < 0.01), and bony resorption over the chin compared to men, who exhibited concentrated bone resorption at the gonion projection.

Conclusions: Mandibular aging, independent of tooth loss, exhibits specific bone remodeling patterns by sex. Posteriorly, mandibular widths increase in both sexes, whereas ramus height decreases. Women experience more resorption at the anterior alveolar surface and chin than men. Statistical shape modeling effectively visualizes these patterns on a population level, bridging the gap between traditional aging research and current understanding.

Fig. 1.

The mandibular landmarks and measurements used for analysis. A, Mandibular landmarks were assigned manually to the 3D mesh surface. B, Mandibular horizontal linear measurements represented the mandibular widths. C, Mandibular vertical linear measurements that were averaged for both sides of the 3D mesh and horizontal measurements. D and E, Mandibular angle measurements were taken between datum planes through 3 points and linear anatomical landmarks.

Fig. 2.

Anteroposterior view of the mandible 3D overlays and heatmaps (point-based analyses) between different age groups in female SSMs. Over the 3 heatmaps, the SDs of the surface differences are 0.398, 0.898, and 0.989 mm, respectively. The older female SSM is hidden to highlight morphological deviations relative to the younger female SSM (visible).

Fig. 3.

Different views of the 3D overlay and heatmap (point-based analysis) between younger (20–39 y) and older (60–79 y) age groups of the female SSMs, with SD surface differences of 0.989 mm.

Fig. 4.

Anteroposterior view of the mandible 3D overlays and heatmaps (point-based analyses) between different age groups in male SSMs. The SDs of the surface differences are 0.5044, 0.7026, and 0.989 mm, respectively. The older male SSM is hidden to highlight morphological deviations relative to the younger male SSM (visible).

Fig. 5.

Different views of the 3D overlay and heatmap (point-based analysis) between younger (20–39 y) and older (60–79 y) age groups of the male SSMs, with SD surface differences of 0.989 mm.

Fig. 6.

Oblique views of the heatmaps of female and male SSMs showcasing gender-specific anatomical changes in the mandible due to aging. A, Male heatmap shows more localized resorption with specific areas of deposition laterally. B, The overall mandibular aging trend in both sexes. C, Female heatmap shows more uniform and pronounced resorption and less marked deposition.