Abstract
Objectives
The aim of the current study was to assess chin characteristics, in terms of soft tissue thickness and mandibular divergency, in a cohort of adult population and explore potential demographic correlations.
Methods
The sample included 465 lateral cephalograms of adult subjects. Cephalometric measurements were recorded to determine the subjects’ anteroposterior and vertical classifications. The soft tissue characteristics of the chin were determined using the upper and lower lip to E line, pogonion to nasion perpendicular, and soft tissue thickness at level of pogonion (Pog), gnathion (Gn) and menton (Me). The differences between the cephalometric parameters based on the age and gender groups as well as the relationship between soft tissue thickness measurements and mandibular divergence angle were statistically analyzed.
Results
ANB angle, soft tissue thickness at the level of Pog point and menton point showed statistically significant differences between male and female subjects (p = 0.00, 0.029, 0.007, respectively). All measured parameters showed statistically significant differences based on the age group except FMA (p = 0.052), L1-MP (p = 0.28), Gn (p = 0.2), and Me (p = 0.42). No significant differences were detected in the mandibular divergency as measured by FMA at different age and gender groups. However, statistically significant differences were detected at different age and gender as measured by SN-GoMe. All parameters showed statistically significant differences among the different mandibular divergency patterns as measured by FMA and SN-GoMe angle except for ANB and Me.
Conclusion
The soft tissue thickness and characteristics of the chin were significantly influenced by age, gender, and malocclusion pattern in the studied sample. These variations are essential considerations for effective orthodontic and orthognathic surgical treatment planning.
Clinical relevance
Malocclusion and mandibular divergence significantly influence the morphology of the chin and surrounding facial structures. This study highlights variations in skeletal and soft tissue parameters across age, gender, and mandibular divergence patterns. These findings are clinically valuable for personalized orthodontic diagnosis and treatment planning and have broader implications in forensic science and anthropological assessments, where accurate interpretation of chin morphology is essential.
Keywords: Chin characteristics, Mandibular divergency, Demographic variation, Soft tissue thickness
Introduction
Chin characteristics is crucial to facial equilibrium and aesthetics. The labio-mental groove, largely determined by the position of the chin and teeth, is a vital component of facial balance and attractiveness [1, 2]. Facial attractiveness is multifactorial and is influenced by a range of biological, psychological, and cultural factors. Certain facial features such as symmetry and well-defined jawline are frequently associated with attractiveness in many cultures. Nowadays, social media has influenced many patients to seek various aesthetic treatments to improve their appearance including procedures to alter chin projection and enhance harmony [1, 2].
In addition to facial harmony, chin characteristics, such as the shape, size, and projection, can influence occlusion and the overall oral health. In children and adolescents, mandibular growth and chin development are tightly related. Functional orthodontic appliances used during growth spurts are mainly utilized to treat skeletal conditions, guide the proper development of the lower jaw and improve the chin’s position. In adults, significant skeletal abnormalities are more challenging, and treatment may require a combination of orthodontics and surgical procedures [3, 4].
One of the main objectives of orthodontic treatment is to persuade balance between the jaws and facial structures. Orthodontic diagnosis and treatment planning therefore requires careful evaluation of the patient’s features on lateral cephalometric radiographs. In order to be critical and descriptive, it is required to express soft tissues and skeletal dimensions, in terms of angles or linear measurements, of each patient and compare it to the population norms. Small differences could be interpreted as normal variations, while the larger differences would indicate structural deviations that need to be addressed [5–7]. The literature consistently reports on the ethnic variations in the measurements of facial profile [8–11]. The aim of the current study was to assess chin characteristics, in terms of soft tissue thickness and mandibular divergency, in adult population and explore potential demographic correlations.
Materials and methods
The sample included 465 pretreatment lateral cephalometric radiographs of adult subjects retrieved from the archive of the postgraduate orthodontic clinic at Riyadh Elm University. The study was approved by the ethical review committee of the university (FUGRP/2020/184/248/249). All radiographs were taken with the head in natural position (NHP), lips and tongue in resting position, with the Frankfurt plane parallel to the floor. The inclusion criteria were cephalometric radiographs of subjects with no history of previous orthodontic treatment, competent lips, permanent dentitions, and throats that are clear in the radiographs. Records with developmental abnormalities or craniofacial anomalies and radiographs with patient positioning errors were excluded from the sample. The radiographs were traced for linear and angular measurements recording using the WebCeph™ program (AssembleCircle Corp., Gyeonggi-do, Republic of Korea) by two investigators. Inter-examiner reliability assessment was done on 10% of cases to assess the degree of agreement among investigators using Cronbach’s alpha test.
Cephalometric landmarks and measurements were recorded to determine the subjects’ anteroposterior and vertical classifications (Table 1). SNA, SNB and ANB angles were used to determine the anteroposterior relationship and classify malocclusion. Subjects were classified based on ANB angles into Class I: 3 ± 2, Class II > 5, and Class III < 1.
Table 1.
Definition of cephalometric points and landmarks used in the study
| Landmark | Description |
|---|---|
| Pogonion (Pog) | Most anterior point of the mandibular symphysis |
| Gnathion (Gn) | Most anteroinferior point of the mandibular symphysis |
| Menton (Me) | Most inferior point of the mandibular symphysis midline |
| SNA | The angle between sella–nasion plane (SN) and nasion–point A line (NA). It indicates if the maxilla is normal, prognathic, or retrognathic |
| SNB | The angle between sella–nasion plane (SN) and nasion–point B line (NB). It indicates if the mandible is normal, prognathic, or retrognathic |
| ANB | The angle between nasion–point A line (NA) and nasion–point B line (NB). It indicates whether the skeletal relationship between the maxilla and mandible is a normal skeletal class I, II or III |
| FMA | Frankfort to mandibular plane angle. Indicates the amount of mandibular divergency. This angle is formed between the Frankfort horizontal plane as drawn from orbitale to porion intersected with the mandibular plane as drawn from gonion to menton |
| SN-GoMe | This represents the mandibular divergency to the anterior cranial base and is formed by projecting a plane from the sella–nasion line in correlation to a line drawn from Go point to Me point |
| U1-PP | The angle between the long axis of the most protruded maxillary incisor and the ANS-PNS (PP) line |
| L1-MP (IMPA) | The angle between the long axis of the most protruded mandibular incisor and the mandibular plane |
| Upper lip to E line | Line drawn from tip of the nose (pronasale) to the tip of the chin (soft tissue pogonion) in correlation with the upper lip |
| Lower lip to E line | Line drawn from tip of the nose (pronasale) to the tip of the chin (soft tissue pogonion) in correlation with the lower lip |
| Pog to N perp | Distance between the line soft tissue pogonion and a perpendicular line to Frankfort tangent to the soft tissue nasion |
| Pog–Pog’ | The soft tissue thickness of chin at level of pogonion was measured as the horizontal distance between pogonion (Pog) and soft tissue pogonion (Pog’) points |
| Gn–Gn’ | Soft tissue thickness of chin at level of gnathion was measured as the horizontal distance between gnathion (Gn) and soft tissue gnathion (Gn’) points |
| Me–Me’ | Soft tissue thickness of chin at level of menton was measured as the horizontal distance between menton (Me) and soft tissue menton (Me’) points |
The vertical classification was divided according to Frankfort to mandibular plane angle (FMA) and sella–nasion to gonion–menton angle (SN-GoMe) into normo-divergent (FMA 22–280 or SN-GoMe 27–36°), hyper-divergent (above the norm) and hypo-divergent (below the norm). The dental relationship was determined using U1-PP, L1-MB and the interincisal angle. The soft tissue characteristics of the lips and chin were determined using the upper and lower lip to E line, pogonion to nasion perpendicular, and soft tissue thickness at level of pogonion (Pog), gnathion (Gn) and menton (Me) measurements (Table 1 and Fig. 1).
Fig. 1.
Cephalometric landmarks and parameters used in the study
Statistical analysis
Post hoc power analysis indicated that a sample size of 465 pretreatment lateral cephalometric radiographs provided greater than 90% power to detect small to moderate effect sizes at an alpha level of 0.05 for comparisons among mandibular divergence groups. This suggests that the study was sufficiently powered to identify statistically significant differences in soft tissue thickness measurements across groups. Intraclass correlation coefficient (ICC) test was utilized to determine interrater reliability. A two-way analysis of variance and post hoc tests (Bonferroni) were used for multiple comparisons (cephalometric linear and angular measurements). Student’s t-test was utilized to assess the difference between the cephalometric parameters based on the age groups. Kruskal–Wallis one-way analysis of variance was used to compare the studied parameters between male and female subjects. Comparison of differences between genders within each group was conducted using Mann–Whitney test. The Pearson correlation coefficient gauged the relationship between soft tissue thickness measurements and mandibular divergence angle. A p-value of less than 0.05 was considered significant and the data were analyzed using IBM-SPSS for Windows version 28.0 (SPSS Inc., Chicago, IL).
Results
The ICC test indicated an inter-examiner reliability of almost 0.84 for all measurements. A total of 465 subjects were included in the final analysis (45.5% male and 54.5% female, mean age of 19.26 ± 7.13 years). Descriptive statistics of the included sample and the selected cephalometric parameters are presented in Tables 2 and 3. The subjects were categorized based on their AP and vertical classifications (Fig. 2).
Table 2.
Pogonion (Pog–Pog’), gnathion (Gn–Gn’), and menton (Me–Me’) measurements according to the AP and vertical classifications
| n | Mean | SD | |||
|---|---|---|---|---|---|
| ANB° | Class I | 193 | Pog | 12.06 | 2.61 |
| Gn | 9.06 | 2.52 | |||
| Me | 7.46 | 2.17 | |||
| Class II | 180 | Pog | 12.69 | 3.50 | |
| Gn | 9.11 | 2.79 | |||
| Me | 7.27 | 2.04 | |||
| Class III | 103 | Pog | 12.74 | 3.00 | |
| Gn | 9.74 | 2.61 | |||
| Me | 8.39 | 2.28 | |||
| FMA° | Normo-divergent | 161 | Pog | 12.12 | 3.06 |
| Gn | 9.07 | 2.60 | |||
| Me | 7.56 | 2.18 | |||
| Hyper-divergent | 217 | Pog | 12.18 | 2.51 | |
| Gn | 8.74 | 2.40 | |||
| Me | 7.42 | 2.15 | |||
| Hypo-divergent | 98 | Pog | 13.57 | 3.81 | |
| Gn | 10.56 | 2.85 | |||
| Me | 8.01 | 2.23 | |||
| SN-GoMe° | Normo-divergent | 180 | Pog | 12.30 | 2.89 |
| Gn | 9.22 | 2.60 | |||
| Me | 7.69 | 2.34 | |||
| Hyper-divergent | 232 | Pog | 12.14 | 2.57 | |
| Gn | 8.84 | 2.34 | |||
| Me | 7.43 | 2.00 | |||
| Hypo-divergent | 64 | Pog | 13.97 | 4.42 | |
| Gn | 10.63 | 3.35 | |||
| Me | 7.86 | 2.36 |
Table 3.
Descriptive statistics of the cephalometric measurements
| Variables | Mean | SD | Minimum | Maximum |
|---|---|---|---|---|
| Age | 19.26 | 7.13 | 6.00 | 47.00 |
| SNA° | 82.31 | 4.56 | 62.70 | 99.81 |
| SNB° | 78.70 | 4.50 | 60.16 | 92.78 |
| ANB° | 3.62 | 3.68 | − 11.69 | 14.19 |
| FMA° | 27.44 | 6.70 | 3.70 | 45.96 |
| Sn_GoMe° | 35.73 | 7.49 | 9.19 | 55.83 |
| U1_PP° | 116.85 | 7.70 | 95.80 | 143.95 |
| L1_MP (IMPA) ° | 92.60 | 10.62 | 58.44 | 133.25 |
| Ulip_E line (mm) | − 1.97 | 3.03 | − 13.93 | 7.55 |
| L lip_E line (mm) | 0.70 | 2.89 | − 11.28 | 11.86 |
| Pog_N perp (mm) | − 4.65 | 7.48 | − 32.44 | 19.08 |
| Pog | 12.44 | 3.06 | 1.92 | 30.74 |
| Gn | 9.23 | 2.65 | 3.66 | 23.01 |
| Me | 7.59 | 2.19 | 2.59 | 18.35 |
Fig. 2.
Anteroposterior and vertical classification of the included sample
According to the FMA values, the sample distribution was 33.8% normo-divergent; 45.6% hyper-divergent, and 20.6% hypo-divergent. The SN-GoMe values indicated a distribution of 37.8% normo-divergent, 47.6% hyper-divergent, and 13.4% hypo-divergent (Table 2).
ANB angle, soft tissue thickness at the level of Pog point and menton point showed statistically significant differences between male and female subjects (p = 0.00, 0.029, 0.007, respectively) while all other measured parameters did not show significant differences (Table 4). All measured parameters showed statistically significant differences based on the age group except FMA (p = 0.052), L1-MP (p = 0.28), Gn (p = 0.2), and Me (p = 0.42) (Table 5).
Table 4.
Comparison of cephalometric measurements based on gender
| Variables | Male | Female | p | ||
|---|---|---|---|---|---|
| Mean | SD | Mean | SD | ||
| SNA° | 82.07 | 4.75 | 82.52 | 4.40 | 0.28 |
| SNB° | 79.09 | 4.87 | 78.37 | 4.14 | 0.087 |
| ANB° | 2.92 | 3.88 | 4.21 | 3.40 | 0.000 |
| FMA° | 26.93 | 6.53 | 27.87 | 6.83 | 0.128 |
| Sn_GoMe° | 35.10 | 7.44 | 36.27 | 7.51 | 0.089 |
| U1_PP° | 117.15 | 8.12 | 116.60 | 7.34 | 0.439 |
| L1_MP (IMPA) ° | 91.93 | 10.75 | 93.17 | 10.50 | 0.202 |
| Ulip_E line (mm) | − 2.20 | 3.28 | − 1.77 | 2.79 | 0.131 |
| L lip_E line (mm) | 0.63 | 2.94 | 0.76 | 2.85 | 0.620 |
| Pog_N perp (mm) | − 4.22 | 7.77 | − 5.01 | 7.23 | 0.254 |
| Pog | 12.78 | 3.17 | 12.17 | 2.93 | 0.029 |
| Gn | 9.36 | 2.64 | 9.11 | 2.66 | 0.299 |
| Me | 7.89 | 2.29 | 7.34 | 2.06 | 0.007 |
Table 5.
Comparison of cephalometric measurements based on age group
| Variables | ≤ 17 | > 17 | p | ||
|---|---|---|---|---|---|
| Mean | SD | Mean | SD | ||
| SNA° | 81.73 | 4.30 | 82.90 | 4.74 | 0.005 |
| SNB° | 77.71 | 4.29 | 79.68 | 4.49 | 0.000 |
| ANB° | 3.99 | 3.33 | 3.25 | 3.97 | 0.028 |
| FMA° | 28.04 | 6.92 | 26.85 | 6.45 | 0.052 |
| Sn_GoMe° | 36.75 | 7.57 | 34.73 | 7.29 | 0.003 |
| U1_PP° | 116.13 | 7.32 | 117.56 | 8.01 | 0.042 |
| L1_MP (IMPA) ° | 92.08 | 10.24 | 93.13 | 10.98 | 0.282 |
| Ulip_E line (mm) | − 1.20 | 2.69 | − 2.72 | 3.16 | 0.000 |
| L lip_E line (mm) | 1.13 | 2.80 | 0.28 | 2.92 | 0.001 |
| Pog_N perp (mm) | − 5.89 | 6.99 | − 3.42 | 7.77 | 0.000 |
| Pog | 12.08 | 2.78 | 12.81 | 3.27 | 0.009 |
| Gn | 9.07 | 2.51 | 9.38 | 2.79 | 0.202 |
| Me | 7.51 | 2.10 | 7.67 | 2.27 | 0.420 |
No statistically significant differences were detected in the mandibular divergency as measured by FMA at different age groups and different gender. However, statistically significant differences were detected at different age and gender as measured by SN-GoMe (Table 6). In addition, all parameters showed statistically significant differences among the different mandibular divergency patterns as measured by FMA and SN-GoMe angle except for ANB and Me (Tables 7 and 8).
Table 6.
Distribution of divergence based on the FMA and Sn-GoME
| Variables | FMA | Sn-GoMe | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Normo | Hyper | Hypo | p | Normo | Hyper | Hypo | p | |||||||||
| n | % | n | % | n | % | n | % | n | % | n | % | |||||
| Age | ≤ 17 | 71 | 44.10 | 121 | 55.80 | 45 | 45.90 | 0.056 | 88 | 48.90 | 126 | 54.30 | 23 | 35.90 | 0.032 | |
| > 17 | 90 | 55.90 | 96 | 44.20 | 53 | 54.10 | 92 | 51.10 | 106 | 45.70 | 41 | 64.10 | ||||
| Total | 161 | 100.00 | 217 | 100.00 | 98 | 100 | 180 | 100.00 | 232 | 100.00 | 64 | 100.00 | ||||
| Gender | Male | 82 | 50.90 | 90 | 41.50 | 45 | 45.90 | 0.188 | 97 | 53.90 | 90 | 38.80 | 30 | 46.90 | 0.009 | |
| Female | 79 | 49.10 | 127 | 58.50 | 53 | 54.10 | 83 | 46.10 | 142 | 61.20 | 34 | 53.10 | ||||
| Total | 161 | 100.00 | 217 | 100.00 | 98 | 100 | 180 | 100.00 | 232 | 100.00 | 64 | 100 | ||||
Table 7.
Categories of divergence and cephalometric variables based on FMA
| Variables | Normo-divergence | Hyper-divergence | Hypo-divergence | p | |||
|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||
| SNA° | 82.97 | 4.15A | 80.79 | 4.16B | 84.61 | 4.87C | < 0.001 |
| SNB° | 79.55 | 4.08A | 76.82 | 3.96B | 81.46 | 4.47C | < 0.001 |
| ANB° | 3.46 | 3.53 | 3.97 | 3.18 | 3.13 | 4.74 | 0.133 |
| FMA° | 25.35 | 1.75A | 33.22 | 3.78 B | 18.09 | 3.60C | < 0.001 |
| Sn_GoMe° | 33.82 | 3.75A | 41.51 | 4.80B | 26.11 | 5.09C | < 0.001 |
| U1_PP° | 117.12 | 7.62A | 114.45 | 6.81B | 121.72 | 7.38C | < 0.001 |
| L1_MP (IMPA) ° | 92.87 | 9.30A | 88.42 | 7.93B | 101.42 | 12.34C | < 0.001 |
| Ulip_E line (mm) | − 2.20 | 3.13A | − 1.38 | 2.86B | − 2.88 | 2.96A | < 0.001 |
| L lip_E line (mm) | 0.30 | 2.72A | 1.43 | 2.88B | − 0.23 | 2.80A | < 0.001 |
| Pog_N perp (mm) | − 2.30 | 6.48A | − 8.75 | 5.98B | 0.57 | 7.14C | < 0.001 |
| Pog | 12.12 | 3.06A | 12.18 | 2.51A | 13.57 | 3.81B | < 0.001 |
| Gn | 9.07 | 2.60A | 8.74 | 2.40A | 10.56 | 2.85B | < 0.001 |
| Me | 7.56 | 2.18 | 7.42 | 2.15 | 8.01 | 2.23 | 0.082 |
Table 8.
Categories of divergence and cephalometric variables based on Sn-GoMe
| Variables | Normo | Hyper | Hypo | p | |||
|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||
| SNA° | 83.53 | 3.94A | 80.25 | 3.82B | 86.4 | 4.73C | < 0.001 |
| SNB° | 80.08 | 3.9A | 76.39 | 3.6B | 83.2 | 4.05C | < 0.001 |
| ANB° | 3.41 | 3.52 | 3.86 | 3.19 | 3.35 | 5.38 | 0.395 |
| FMA° | 24.85 | 3.36A | 32.23 | 4.85B | 17.4 | 3.76C | < 0.001 |
| Sn_GoMe° | 32.38 | 2.43A | 41.82 | 4.21B | 23.1 | 3.42C | < 0.001 |
| U1_PP° | 117.99 | 6.74A | 114.1 | 7.05B | 124 | 7.51C | < 0.001 |
| L1_MP (IMPA) ° | 94.08 | 8.4A | 87.92 | 8.2B | 105 | 12.28C | < 0.001 |
| Ulip_E line (mm) | − 2.35 | 2.99A | − 1.52 | 2.94B | − 2.5 | 3.26A | 0.007 |
| L lip_E line (mm) | 0.16 | 2.84A | 1.24 | 2.82B | 0.29 | 2.96A | < 0.001 |
| Pog_N perp (mm) | − 2.87 | 6.92A | − 7.31 | 6.94B | 0.01 | 7.24C | < 0.001 |
| Pog | 12.3 | 2.89A | 12.14 | 2.57A | 14 | 4.43B | < 0.001 |
| Gn | 9.22 | 2.6A | 8.84 | 2.34A | 10.6 | 3.35B | < 0.001 |
| Me | 7.69 | 2.34 | 7.43 | 2 | 7.86 | 2.36 | 0.280 |
Discussion
Mandibular divergency and chin characteristics differ among ethnic groups, with implications for orthodontic and orthognathic surgical practices. Understanding these anatomical differences is critical, as they can significantly influence treatment planning, aesthetic outcomes, and functional occlusion in patients. Furthermore, variations in chin soft tissue thickness and mandibular divergence have been shown to correlate with different skeletal malocclusion types and facial growth patterns, highlighting the necessity for tailored treatment approaches in diverse populations [12, 13]. The aim of the current study was to assess the variations in chin characteristics in a cohort of adults and explore potential demographic correlations. WebCeph, a cloud-based cephalometric analysis software, was utilized in this study for its convenience and automated landmark detection capabilities. Its key advantages include the user-friendly interface and time efficiency. Its automated analysis functions can reduce human error and improve reproducibility. However, the software offers limited flexibility in terms of customization and integration with other digital workflows compared to some desktop-based alternatives [14].
The study included cephalometric analysis of pretreatment radiographs of 465 adults categorized based on their skeletal sagittal malocclusion and facial divergence types. The results indicated significant differences in ANB angle as well as soft tissue thickness of the chin between male and female subjects. In addition, most of the measured parameters showed significant variation between age groups. These findings are in agreement with previous literature reporting that male subjects typically exhibit a wider, more angular chin compared to female subjects who often present with a taller, more rounded chin structure [13, 15]. Literature has also consistently indicated that mandibular structural and morphological differences are influenced by factors such as facial type, ethnicity, and age [16–18].
Within the field of anthropology, there is broad consensus that the concept of universal face balance and attractiveness is challenged by the significant geographic variations in several facial features including the chin. These variations point to either genetic drift or sexual selection that is region-specific [16, 17, 19]. The study by Okumura et al., confirmed ethnic differences in chin characteristics with their results indicating that Koreans demonstrate larger chin volumes compared to Egyptians [17].
The results of the current study also revealed that mandibular divergence patterns, as measured by SN-GoMe, were significantly affected by age and gender classifications. However, they did not show significant difference when measured by FMA. The difference in anatomical landmarks chosen for each parameter may account for the discrepancy. SN-GoMe was found to be a more accurate indicator of mandibular divergence compared to other parameters, including FMA, in several studies [20, 21]. In a study by Ahmed et al., SN-GoMe showed higher reliability, sensitivity, and positive predictive value in assessing vertical growth patterns, particularly in hyper-divergent cases. Therefore, SN-GoMe could be considered a more accurate and reliable parameter for evaluating mandibular divergence [22].
The discrepancies observed in vertical classification using FMA and SN-GoMe suggest the importance of considering multiple angular measurements when evaluating mandibular divergence. These differences reflect the underlying anatomical variations and highlight the need for a comprehensive diagnostic approach rather than reliance on a single angle. Clinically, awareness of such discrepancies is essential in treatment planning, as skeletal growth direction and vertical dimension control play crucial roles especially when planning surgical interventions.
It is well recognized that the morphologic structure of the chin varies depending on malocclusion pattern. Class II malocclusions often show reduced mandibular dimensions and distinct soft tissue profiles compared to Class I and III. Subjects with skeletal Class II asymmetry show smaller condyle angle, condylar height, ramus and mandibular body length on the deviated side compared to the contralateral side. Class I malocclusion is also known to be associated with a larger chin-lip angle and shallower mentolabial furrow compared to Class II. Conversely, Class III malocclusion shows a smaller chin-lip angle and a deeper mentolabial furrow [23–25]. The findings of the current study concerning adult population, confirms that malocclusion classification significantly affects the characteristics and morphology of the chin and that the different classes of malocclusion (I, II, and III) exhibit distinct chin hard and soft tissues features. Recognizing these distinctions is essential for precise diagnosis and efficient treatment planning.
This study highlights the importance of assessing chin soft tissue thickness in relation to mandibular divergence for informed facial diagnosis and treatment planning. In orthodontic cases, recognizing soft tissue limitations can guide decisions to minimize adverse profile changes. In cases with severe skeletal discrepancies, soft tissue evaluation aids in determining whether surgical intervention may yield better aesthetic outcomes. Incorporating soft tissue analysis into treatment planning enhances the precision and predictability of both orthodontic and orthognathic outcomes.
The study has several limitations that should be acknowledged. First, the use of a single-institution sample may limit the generalizability of the findings. Second, the retrospective nature of the study introduces inherent limitations such as selection bias and incomplete data records, which may affect the reliability of some variables. Additionally, potential confounding factors could not be fully controlled due to the non-randomized design. Future multicenter, prospective studies with larger and more diverse populations are recommended to validate and extend the findings of this research.
Conclusion
The soft tissue thickness and characteristics of the chin vary significantly among populations and with the different classes of malocclusion. These disparities are also significantly influenced by age and gender. Recognizing these variations can aid clinicians in predicting soft tissue responses, improving facial aesthetic outcomes, and making more informed decisions regarding treatment modalities.
Author contributions
Conceptualization, N.T. and R.A.; Methodology, N.T., F.A. and S.A. X.X.; Validation, N.T., S.A. and A.B.; Formal Analysis, A.B.; Investigation, F.A. and E.A.; Resources, N.T.; Data Curation, R.A.; Writing—Original Draft Preparation, E.A. and N.T.; Writing—Review & Editing, E.A., N.T., R.A., and F.A.
Funding
None.
Data availability
Data will be made available by the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the research ethics committee at Riyadh Elm University (FUGRP/2020/184/248/249).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available by the corresponding author on reasonable request.