Facial Soft Tissue Characteristics Among Sagittal and Vertical Skeletal Patterns: A Cone-Beam Computed Tomography Study

Abstract

Background Facial esthetics depend on the skeletal and dental structures underlying variable facial soft tissue thickness. In this social context, determining the relationship between external soft tissue and underlying skeletal and dental hard tissue is essential for detailed orthodontic diagnosis and treatment planning. Objective This study aims to measure facial soft tissue thickness in different sagittal and vertical skeletal patterns. Methodology This is an observational study utilizing pre-existing cone-beam computed tomography (CBCT) images of 170 subjects (110 females and 60 males) with a mean age group of 37.45 ± 13.83 years. CBCT images were then classified sagittally based on the point A-Nasion-point B (ANB) angle from Steiner's analysis into skeletal Class I, Class II, and Class III. Furthermore, vertical patterns were grouped based on the Frankfort-mandibular plane angle (FMA) from Tweed's analysis into hyperdivergent, hypodivergent, and normodivergent facial types. One-way ANOVA was used to compare the means of facial soft tissue thickness between the skeletal groups, followed by Tukey's post-hoc test for individual comparison. Multinomial logistic regression analysis was used to test the association between gender, age, and skeletal groups. The significance level was 0.05. Results One-way ANOVA revealed statistically significant differences in both sagittal and vertical groups (p≤0.05). Tukey's post hoc analysis showed that the skeletal Class III group has increased soft tissue thickness in the subnasale, upper lip, and mention compared to Class I and Class II subjects. Moreover, the hypodivergent group demonstrated increased soft tissue thickness in gnathion and mentioned landmarks in relation to the other groups. Multinomial logistic regression analysis showed significant differences between groups according to both gender and sagittal skeleton patterns (p≤0.05), with males less likely to be in Class II. Conclusions Skeletal Class III and hypodivergent groups have thicker soft tissue in specific facial landmarks. Sexual dimorphism was marked in soft tissue measurements.

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

“…Soft tissues remain an important factor in facial recognition, since, regardless of the method used, the thickness of facial soft tissue can mask skeletal alterations. Therefore, when reconstruction is performed using AI, it must include bone and soft tissues [ 8 , 43 ]. Alhazmi et al [ 43 ] conducted a study of soft facial tissues between different sagittal skeletal patterns, observing that males with facial hypodivergence and class III show greater soft tissue thickness when compared to class I or class II skeletal conditions.…”