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.

Takeaways

Question: How do men’s and women’s mandibles age?

Findings: Artificial intelligence–assisted 3-dimensional analyses of 280 computed tomography scans (ages 20–79 y) revealed subtle but sex-specific mandibular changes. Posterior mandibular height decreased in both sexes. Men showed more bone resorption and flattening of the antegonial notch, whereas women showed more symphyseal resorption. Both sexes exhibited increased bigonial and intercondylar widths posteriorly.

Meaning: There were subtle yet sex-specific changes in mandibular shape with aging. Men showed less mandibular angle definition with aging, whereas women had more bone resorption along the chin area. These findings provide more insights for surgeons to optimize outcomes in facial rejuvenation.

INTRODUCTION

The face constitutes our personal identity and plays a pivotal role in social interactions and aesthetic perception.^1,2 Aging is an inevitable multifactorial process affecting bony structures and overlying soft tissues, manifesting as visible bony contour changes, deep wrinkles, and tissue sagging.³ A detailed understanding of these anatomical shifts allows clinicians and researchers to design targeted interventions that address both the aesthetic and functional impacts of facial aging.^1,2 However, the potential aesthetic contributions of average facial skeletal aging have often been overlooked, as historically, research has focused more on pathological conditions like osteoporotic changes or edentulism.^4–6

Over the last 3 decades, there has been a growing recognition of the role of the facial skeleton in aesthetic rejuvenation.^2,7–9 This shift in focus challenges the previous belief that facial aging was solely due to soft tissue changes.¹⁰ Studies have indicated a flattening of facial contours due to facial bony resorption.^6,11–14 However, aging involves more than mere bone recession; it also includes loosening of supportive soft tissues, leading to a gradual loss of facial structure and definition.^15–17 This is especially evident in the lower facial third, where the mandible outlines the jaw contour and marks the transition to the neck.^7,17–19 Thus, natural bone remodeling of the mandible leads to visible aging changes in the lower facial height and contributes to sagging of the overlying structures, which causes jowling and diminished jawline definition.^{6,7,9,17,18,20} Consequently, many researchers reported their original observations of mandibular aging through anthropometric analyses and 2-dimensional (2D) illustrative artwork.^{1,2,7,18,19,21}

In facial rejuvenation, evolving techniques such as soft tissue lifting, volumizing with dermal fillers, fat grafting, and bony augmentation with mandibular implants (including genioplasty) are continually refined to better meet the patients’ aesthetic needs.^22,23 With such an abundance of cosmetic options, surgeons advocate a time-honored principle, “Losses must be replaced in kind,”²⁴ by Sir Harold Gilles.²⁵ Thus, volume restoration theory is widely advocated to achieve favorable aesthetic outcomes.^{2,6,7,9,16–18,20,26,27} In our study, the concept of “averageness,” as previously described by Lambros and Amos²⁸ and Lambros,²⁹ is embraced to facilitate the comparison of general skeletal aging trends. Advanced 3D shape analysis of facial computed tomography (CT) scans has, for the first-time, enabled visualization of complex aging patterns in the mandible,³⁰ highlighting subtle morphological changes.¹⁴

METHODS

This retrospective study was approved by our institutional review board. Facial CT scans from 2011 to 2023 were extracted from our database, including individuals above 5 feet 5 inches of White ethnicity (non-Hispanic or Latino) to ensure dataset uniformity. The indications for CT imaging included stroke/transient ischemic attack, headaches, and dizziness. Exclusion criteria comprised trauma or orthodontic intervention, edentulous patients, deformities, and poor-quality scans. The final sample included 280 subjects, categorized into 3 age groups: younger (20–39 y), middle (40–59 y), and older (60–79 y). The younger group consisted of 94 individuals (49 women and 45 men), the middle group included 80 participants (45 women and 35 men), and the older group was composed of 106 individuals, with an equal number of women and men (53 each) (Table 1). Excluding many 3D meshes with torus mandibularis³¹ yielded a marginally smaller sample for the middle-aged male group. The distribution was balanced by age and sex using random sampling.

Table 1.

Average Age and BMI in Each Age Category

Characteristics	Younger Group, 20–39 y		Middle Group, 40–59 y		Older Group, 60–79 y
Sex	F (n = 49)	M (n = 45)	F (n = 45)	M (n = 35)	F (n = 53)	M (n = 53)
Mean age, y (SD)	31.39 (±4.78)	32 (±4.96)	51.18 (±5.31)	51.81 (±6.38)	67.87 (±6.15)	68.28 (±5.94)
Mean BMI, kg/m2 (SD)	30.45 (±9.78)	30.29 (±6.66)	30.21 (±7.02)	29.71 (±6.43)	29.21 (±5.94)	30.5 (±7.04)

Three-dimensional Reconstruction and Statistical Shape Modeling

Using machine learning (ML) scripts, 280 mandibles were reconstructed in Mimics Innovation Suite 25 (Materialise NV, Belgium), including 147 women and 133 men (Table 1). These ML scripts were used for accurate automatic delineation of mandibular bones with threshold around 226 Hounsfield units, significantly reducing user variability. Manual removal of dentition was precisely conducted, as the alveolar-teeth demarcation was clearly visualized on the radiographic imaging as well as the 3D meshes. (See Figure, Supplemental Digital Content 1, which describes the application of ML scripts for autosegmentation of the facial skeletal bones. Manual removal of teeth was then conducted to minimize the geometrical variability of the 3D meshes, in preparation for the SSM analysis, http://links.lww.com/PRSGO/D936.)

All 3D models were then exported in .stl* format to 3-matics v.17. The statistical shape modeling (SSM) function was used to create average models for each group, detailed in Video 1. (See Video 1 [online], which displays the preparation of SSM for 3D shape analysis. High-resolution 3D mandibles were grouped by age and sex, aligned to a master 3D mesh using anatomical landmarks, and registered through 10-point iterations. The final SSM visualizes the average shape of the studied population.) In 3-matics, average mandibular SSMs were aligned by sex and age group using manual positioning and the iterative closest point algorithm, typically more than 10 iterations approximation. Surface differences were visualized using 3D overlays and heatmaps, which displayed deviations from a reference model to detect aging trends. The heatmap scale ranged from red to green to blue. The histogram illustrated the frequency distribution, showing a concentration of data around zero with a normal distribution pattern. (See figure, Supplemental Digital Content 2, which displays the heatmap [point-based analysis] that was generated to visualize the deviations of mandibular structures from a reference model. The color scale ranged from −2.5000 to 2.5000, with blue indicating the minimum deviations and red indicating the maximum deviations. The histogram illustrated the frequency distribution, http://links.lww.com/PRSGO/D937.) In the context of aging, the red color corresponds to bone resorption (inward), whereas the blue hue corresponds to bone expansion (outward) surface deviations, with a variation of around 2.5 mm.²⁹

Video 1. displays the preparation of SSM for 3D shape analysis. High-resolution 3D mandibles were grouped by age and sex, aligned to a master 3D mesh using anatomical landmarks, and registered through 10-point iterations. The final SSM visualizes the average shape of the studied population.

Download video file ^{(23.4MB, mp4)}

Anthropometric Data Collection

Linear measurements were used for the 3D SSMs.^{11,13,14,17,19,32–36} Recognizing one of the frequently noted limitations of 2D measurements on 3D reconstructions,¹⁴ the senior author (B.A.S) confirmed 34 mandible bony landmarks (Fig. 1A). All these points were designated on the 3D meshes’ surfaces to account for the 3D trajectory and orientation. Axial, coronal, and sagittal CT views were simultaneously visualized, ensuring precise landmark placement and minimizing visual perception errors. The distances, lines, and angles were then obtained according to the world coordinate distance (XYZ) and user-defined coordinate plane using Python 3.8 scripts. (Fig. 1) Interrater reliability (>0.8) was assessed between 2 co-authors (S.M.H. and A.A.S.).

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.

Vertical measurements included ramus height and anterior body height, and horizontal measurements included intercondylar, intercoronoid, bigonial, interlingula, interrami, ramus width (breadth), and mandibular body length. Angular measurements included mandibular angle, mandibular anterior body angle, alveolar angle, and mental angle. To account for the inherent asymmetry of the facial bones,¹⁰ measurements from both sides of the mandible were averaged to yield a single value for each parameter. For angular assessments, we aimed to maintain consistency in the areas selected for measurement across the methodology by using the same marked regions for creating datum planes. This approach ensured reliable comparisons across the same measurements, detailed in Supplemental Digital Content 3.³⁷ (See figure, Supplemental Digital Content 3, which displays the mandibular angle measurements using 3-Matic software, illustrating the steps for angle measurement between datum planes within the XYZ coordinate system. For visualization purposes, the rami plane was hidden, and green stars indicate the centroid-directed trajectory, http://links.lww.com/PRSGO/D938.) The axial plane, which is tangential to the inferior border of the mandible, was additionally used as a reference to standardize horizontal measurements parallel to it. Finally, measurements, summarized in Supplemental Digital Content 4, were exported into Microsoft Excel for further analysis. (See table, Supplemental Digital Content 4, which displays linear and angular measurements of the relevant anthropometric bony landmarks that were collected on the 3D mandibles for further analysis, http://links.lww.com/PRSGO/D939.)

Statistical Analysis

The SSM results included parameters such as minimum, maximum, mean, SD, and cumulative variance to quantify the distribution of shape deviations across the studied population. Furthermore, linear and angular measurements on the SSMs of 3 age groups within each sex were compared using univariate analysis of variance (ANOVA) statistics (Interactive Statistics—one-way ANOVA from summary data [statpages.info]). The Tukey honestly significant difference (HSD) post hoc test was applied for all the statistically significant results. (See table, Supplemental Digital Content 5, which displays pairwise comparisons that were elucidated further in the Tukey HSD post hoc test to highlight specific statistically significant results, http://links.lww.com/PRSGO/D940.). A P value of less than 0.05 and 95% confidence intervals were considered statistically significant.

RESULTS

Our SSM analysis included 280 mandibles stratified into 3 age groups with similar distributions of sex and body mass index (BMI), as detailed in Table 1. The mean age was 31.5 (±4.8) years for the younger group, 51.4 (±5.6) years for the middle group, and 67.4 (±6) years for the older group. The mean BMI was around 30.3 (± 5.1) kg/m² for each group.

The analysis revealed several significant aging-related changes, as depicted in both linear measurements and heatmaps (Figs. 2–5). The superior surface of the condyles, the coronoids, the anterior mandibular body, and the inferior border of the mandible showed a bone resorption trend. Anteriorly, younger women start with a more pointed chin, but with age, broader resorption causes less defined chin contour. In contrast, men experience differential bone loss, which is more pronounced in the alveolar ridge rather than being uniformly distributed (Figs. 2, 4). These changes are also demonstrated in Videos 2–4. (See Video 2 [online], which displays female mandible aging changes. It includes linear and angular measurements, along with a local point analysis on the heatmaps, comparing the younger and older age groups.) (See Video 3 [online], which displays male mandible aging changes. It includes linear and angular measurements, along with a local point analysis on the heatmaps, comparing the younger and older age groups.) (See Video 4 [online], which displays an animation showing mandibular aging in female and male SSMs morph renders, where the younger group SSM are references [semitransparent silhouettes]. It shows the visible continued bony remodeling patterns across different age groups.) Additionally, detailed measurements are presented in Tables 2 and 3 for women and men, respectively.

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. 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. 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).

Table 2.

Difference in Anthropometric Measurements Between Female SSMs

	Younger Group (±5.91)	Middle Group (±4.86)	Older Group (±5.38)	F test	P
Linear measurements	Female SSMs (SD), mm
Ramus heightb,c	69.75	67.5	64.47	12.6	0.00 *
Anterior body height	28.34	28.47	28	0.11	0.9
Intercondylar widthb,c	99.19	100.6	103.6	9.14	0.00 *
Intercoronoid width	91.03	91.51	93.49	3.05	0.05 *
Intergonial (bigonial) widtha,b	80.68	84.83	85.02	10.4	0.00 *
Interlingula width	81.25	82.5	83.05	1.51	0.22
Interrami widthb	77.93	80.04	81.55	5.88	0.00 *
Ramus width (breadth)	32.33	32.27	30.9	5.88	0.31
Mandibular body length	84.88	85.7	84.11	1.06	0.35
Angular measurements	Manual angle mean (SD), degrees
Mandibular angleb	119.3	121.4	123.2	6.81	0.00 *
Mandibular anterior body angleb,c	66.3	65.4	62.2	8.15	0.00 *
Alveolar angle	153.6	154	155.8	2.46	0.09
Mental angle	63.3	64	64.5	0.65	0.52

Tukey HSD post hoc test:

^aStatistically significant difference between younger and middle age groups.

^bStatistically significant difference between younger and older age groups.

^cStatistically significant difference between middle and older age groups.

This table compares linear (mm) and angular (degrees) measurements of average SSMs for 3 female age groups. Values are presented as means with SDs. Annotations (^a, ^b, ^c) indicate significant pairwise comparisons detailed in the Tukey HSD post hoc test (see Supplemental Digital Content 5, http://links.lww.com/PRSGO/D940).

^*Bold values indicate statistically significant differences (P < 0.05).

Table 3.

Difference in Anthropometric Measurements Between Male SSMs

	Younger Group (±5.4)	Middle Group (±5.78)	Older Group (±5.6)	F Test	P
Linear measurements	Male SSMs (SD), mm
Ramus heightb,c	76.65	75.1	72.45	7.09	0.00 *
Anterior body height	31.21	31.34	30.29	0.49	0.61
Intercondylar widthb,c	104.24	106.27	110.75	17.5	0.00 *
Intercoronoid widthb	95.29	97.05	98.09	3.09	0.05 *
Intergonial (bigonial) widtha,b,c	84.59	88.4	91.84	20.6	0.00 *
Interlingula width	88	87.03	87.28	0.31	0.74
Interrami width	82.09	82.31	84.2	2.09	0.13
Ramus width	34.1	34.78	32.79	1.47	0.23
Mandibular body lengtha,b	87.04	91.3	92.8	13.4	0.00 *
Angular measurements	Manual angle mean (SD), degrees
Mandibular angle	122.66	120	121.9	2.32	0.1
Mandibular anterior body angleb,c	63.4	64.7	60.5	6.7	0.00 *
Alveolar anglea,b	154.2	158.9	159.9	13.8	0.00 *
Mental angleb	68.3	65.68	63.77	8.03	0.00 *

Tukey HSD post hoc test:

^aStatistically significant difference between younger and middle age groups.

^bStatistically significant difference between younger and older age groups.

^cStatistically significant difference between middle and older age groups.

This table compares linear (mm) and angular (degrees) measurements of average SSMs for three male age groups. Values are presented as means with SDs. Annotations (^a, ^b, ^c) indicate significant pairwise comparisons detailed in the Tukey HSD post hoc test (see Supplemental Digital Content 5, http://links.lww.com/PRSGO/D940).

^*Bold values indicate statistically significant differences (P < 0.05).

Video 2. displays female mandible aging changes. It includes linear and angular measurements, along with a local point analysis on the heatmaps, comparing the younger and older age groups.

Download video file ^{(17.6MB, mp4)}

Video 3. displays male mandible aging changes. It includes linear and angular measurements, along with a local point analysis on the heatmaps, comparing the younger and older age groups.

Download video file ^{(16MB, mp4)}

Video 4. displays an animation showing mandibular aging in female and male SSMs morph renders, where the younger group SSM are references (semitransparent silhouettes). It shows the visible continued bony remodeling patterns across different age groups.

Download video file ^{(12.6MB, mp4)}

The characteristic gonion projection, which defines the angular prominence of the male jawline, exhibited a flattening and bone resorption trend with aging up to 3 mm (Figs. 3, 5) (Video 4 [online]). Conversely, a widening trend over the intercondylar, interrami, and bigonial widths was observed with aging. Overall, these findings reflect ongoing bone remodeling with aging.

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.

Mandibular Ramus and Body Height

Significant decreases in ramus height were observed (P < 0.001), decreasing in women from 69.75 to 64.47 mm and in men from 76.65 to 72.45 mm. This resorption was mostly in the condylar, coronoid process, and inferior border of the mandibular body regions, consistent with the heatmap analysis (Figs. 2, 5). However, anterior body height remained relatively stable with aging in dentate female and male SSMs, with P values of 0.9 and 0.61, respectively.

Mandibular Width

Widening of the mandible was evident in the older groups with notable increases in intercondylar, coronoids, rami, lingula, and gonial regions. Particularly, bigonial width increased by 4.3–7.3 mm (P < 0.001) in women and men, respectively. This widening trend was more pronounced in men than in women (Videos 2, 3 [online]). On the heatmap, it was represented by deep blue color over the upper half of the ramus of both the aging female and male SSMs (Figs. 3, 5).

Mandibular Body Length

Mandibular body length increased by approximately 5.8 mm in older men compared with the younger group (P < 0.001), likely due to overall continued bone growth and concentrated resorption in the gonions, which shifted gonial landmarks posteriorly and may have impacted body length measurements. In contrast, women showed relatively stable or slightly reduced mandibular body length, despite more observed resorption along the mandibular inferior border and chin (P = 0.35).

Mandibular (Gonial) Angle

In women, the mandibular angle became more obtuse with age, changing from 119.3 degrees in younger women to 123.2 degrees in older women (P < 0.001), which suggests a softening of the jawline with age. Men, however, showed a stable mandibular angle with no statistically significant age-related changes across groups, with averages of 122.66, 120, and 121.9 degrees in the younger, middle, and older groups (P = 0.1), respectively.

Alveolar Angle

It is notable that only dentate mandibular CT scans were included to exclude bone loss due to edentulism. The alveolar angle showed a widening trend due to some alveolar bone resorption with the statistically significant changes in men (P = 0.00). Over the heatmaps, significant bone resorption is observed over the alveolar process for both men and women, as visualized in Figures 2 and 4. Men showed more localized and pronounced changes over the alveolar ridge, with some resorption extending slightly to the menton inferiorly, whereas women exhibited a more uniformly distributed, hourglass-shaped resorption pattern across the entire anterior surface with no significant change (P = 0.09).

Anterior Mandibular Body Angle

Assessment of the anterior surface showed a slight reduction in the anterior mandibular body angle with age, decreasing by 4.1 degrees in women (P < 0.001) and 2.9 degrees in men (P < 0.001). Although retaining teeth tends to preserve the overall height and mandibular structure, there is still a slight inward angulation observed in the anterior body with aging (Figs. 3, 5). This angle degree varied depending on the marking endpoint (gnathion versus menton); however, the trend remained consistent, as shown in Supplemental Digital Content 3 (http://links.lww.com/PRSGO/D938).

Mental Angle

Angular measurements revealed variable trends. No significant differences were observed among the age groups for the mental angle measurements in women (P = 0.52). A statistically significant yet clinically insignificant decrease was noted in men, with 68.3 degrees in the younger and 63.8 degrees in the older groups (P < 0.01).

DISCUSSION

Our analysis of the mandible SSMs using 280 facial CT scans revealed significant patterns of bone resorption and expansion, demonstrating its value in capturing 3D volumetric changes over extended time at a population level. Thus, aging-related changes extend beyond the oversimplified traditional anthropometric measurements derived from 2D imaging modalities, offering a deeper anatomical understanding to inform precise rejuvenation strategies.^{6,17,32,38–40} Historically, facial aging has been understood primarily through artistic illustrations; however, our shape analysis offers a more detailed view of facial morphology than previously documented.^2,14,36,41 To our knowledge, this is the first study to harness the power of artificial intelligence to assist in the segmentation of facial bones in a large sample size and derive 3D morphological aging changes, building upon prior insights.^{2,7,17,19,24,36,42}

Because tooth loss impacts lower facial height,^40,43,44 edentulism has led to notable decrease in mandibular height.^43,44 Our study showed no significant difference in anterior body height, as we excluded edentulous subjects. On the other side, bone loss was observed on the superior surface of the condyles, coronoids, and inferior border of the mandible, which led to a decrease in ramus height and shortening of posterior facial height with aging.^2,17 Over the gonion, aging brings distinct changes in both sexes. Shaw et al¹⁷ reported that gonial angle increases by 13.3 degrees in men and 12.1 degrees in women, whereas we observed a smaller increase of 3.9 degrees in women and no significant change in men. In addition, the SSMs 3D overlay also revealed a gradual flattening of the gonion contour in men, with up to 3 mm of concentrated bone resorption, a detail that has not been emphasized enough in previous studies.^17,19,21 Nevertheless, gender differences in mandibular morphology were evident,³⁶ with male mandibles exhibiting significantly larger dimensions than those of women.⁴⁵ The antegonial notch became less defined in men due to this concentrated inferior gonial resorption.^46,47 The resulting contour changes of the angle area in men become more feminine compared with their original masculine structure.^35,45–47 A similar, yet lesser degree of resorption was observed in females (see Video 4 [online]) (Fig. 6). Conversely, women initially had a more pointed chin, typically the most prominent skeletal region in the lower face, and exhibited more pronounced aging changes over time.⁴⁵ Bone resorption occurred earlier and more extensively in women, leading to a less defined chin region as they aged. For instance, this resorption occurs at the attachment of the mentalis muscle, potentially contributes to the development of visible signs of anterior facial aging, and may explain the ptotic aging of the chin due to loss of underlying bony support.⁴⁸ Consequently, these changes markedly influence the overall facial and neck profile, such as pre- and subplatysma adiposity and platysma laxity become more accentuated.^24,48,49 Additionally, several studies underpinned the structural dynamics in the mandibular ligament and its contribution to the jowling phenomenon.^{1,2,7,18,50,51} Likewise, Guo et al¹⁶ recently demonstrated that lower facial fat compartments and ligaments experience intricate patterns of thinning and thickening, with an overall soft tissue tendency to descend obliquely forward.^1,7,18 As a result, a recent systematic review highlighted chin augmentation techniques with local tissue rearrangement, including mentalis tightening and gliding mentoplasty, which were consistently successful in restoring chin contour.

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.

Generally, our findings support “The Principle of Differential Growth,” stating that bone continues to grow through the lifespan.^19,52–54 This principle is reflected in the observed bone expansion patterns that persist beyond early adulthood in both sexes. For example, Fourgeot et al,¹⁴ in their study of 56 patients with a mean 7-year follow-up, reported significant increases in widths across all facial regions in both sexes. Moreover, Lambros’ study found that head width (tragus to tragus) increased by about 5 mm in men and 3 mm in women, with jowl expansion averaging 3.5 mm.²⁹ Our findings similarly showed increase in facial widths with aging, including intercondylar and bigonial widths by 4.4 and 4.3 mm in women and 6.5 and 7.3 mm in men, respectively (see Videos 2, 3 [online]). These findings suggest that, although overall aging trends are consistent with previous studies, the skeletal changes appear more modest when assessed independently of tooth loss, indicating that soft tissue likely plays a more significant role in lower face aging. Therefore, mandibular skeletal augmentation strategies (alloplastic implants, fillers, or fat grafting) should be considered in the context of the patient’s specific facial aesthetic analysis and anatomy.^46,47 Nonetheless, warranting facial harmony and avoiding unnatural skeletal augmentation will prevent the overcorrected look.⁵⁵

Finally, the SSM methodology, as previously emphasized by Pessa et al¹⁹ and Lambros et al,^28,29 provides a reliable averaged model to discern anatomical differences over a substantial sample size. Our validation study demonstrated that the SSM reliably represented the average of 100 mandibles, accurately capturing the key anthropometric measurements. Here, our findings on mandibular aging raise an important question: how does midface skeletal aging compare? This question is guiding ongoing research within our group. Moving forward, high-quality 3D prints of our SSMs will be used to illustrate aging changes, helping patients understand and build confidence in personalized rejuvenation treatments.

LIMITATIONS

Although 3D modeling advances understanding of mandibular aging, the results may not be universally applicable because our cohort included patients rather than healthy individuals. Despite interrater reliability checks, landmark placement is subjective and sensitive to user assessment. Thereby, volumetric analysis, with the heatmaps and a registration error less than 0.5 mm, offers a more consistent alternative for accurately assessing aging changes. Also, the analysis was derived from different individuals, as capturing aging within a single cohort over time poses significant challenges. We acknowledge these limitations as areas for future research.

CONCLUSIONS

The SSM serves as a tool for quantifying volumetric and contour changes of the mandible in 3D over time, offering an unprecedented visual representation of aging-related changes. It reveals complex, sex- and region-specific patterns of bone resorption and expansion, highlighting average skeletal changes in fully dentate individuals based on analysis of 280 CT scans. Posteriorly, mandibular widths increase in both sexes, whereas ramus height decreases. The overall modest skeletal changes, approximately 5 mm, suggest that soft tissue changes play a more prominent role in facial aging in the lower face. Mandibular aging also shows distinct patterns across sexes, impacting lower facial contours. Women experience more resorption at the anterior alveolar surface and chin than men. In women, bone resorption was uniformly distributed in the anterior chin (symphysis region), taking on an hourglass resorption pattern. In men, resorption was more profound at the gonion with the loss of antegonial notch definition. These skeletal changes, combined with overlying soft tissue changes, contribute to the gradual loss of jawline definition with aging.

DISCLOSURE

The authors have no financial interest to declare in relation to the content of this article.

ACKNOWLEDGMENTS

Special thanks to Christian R. Hanson and Erick O. Martinez, special effects designers, for helping with the animations and videos. We also thank the Mayo Clinic’s Department of AI, the Center for Digital Health, and the Office of Diversity, Equity, and Inclusion for their invaluable support to extend our research to various ethnic populations.