Introduction
The objective of this study was to evaluate the influence of orthodontic bracket prescription on smile attractiveness.
Methods
Three women were chosen according to their sagittal skeletal pattern: skeletal Class I, II, or III malocclusion. For each, 3 smiling pictures were taken in frontal, oblique, and lateral views. The maxillary arch was then scanned, and a 3-dimensional digital model was constructed on the OrthoAnalyzer software. The information of 3 orthodontic prescriptions, namely Roth, MBT, and Ricketts, was simulated after the virtual placement of orthodontic brackets on 10 maxillary teeth. The simulations were then superimposed on the smile photographs in the 3 views, creating a total of 27 images. Groups of orthodontists, dentists, and laypeople rated the attractiveness of each smile using the visual analog scale.
Results
This study included 167 evaluators: 54 orthodontists, 54 dentists, and 59 laypersons. In terms of orthodontic prescription, MBT esthetic scores were 4% lower than Roth scores ( P <0.001), and Ricketts scores were 13.6% lower than Roth scores ( P <0.001). In terms of skeletal pattern, Class II and III scores were 15.0% and 16.4% lower than Class I scores, respectively ( P <0.001). Regarding the smiling view, oblique view scores were 3.1% lower than frontal view scores ( P = 0.031), whereas lateral view scores were 8.4% lower than frontal view scores ( P <0.001). In contrast to gender and age, the specialty of the evaluators significantly affected the esthetic rating of the smiles.
Conclusions
The attractiveness of a smile is affected by the prescription of the brackets. The Roth prescription tends to obtain the most favorable esthetic scores, especially in sagittal smiling images. Profile smiles with excessively proclined incisors are considered unattractive. Different views of the same smile do not get similar esthetic scores because sagittal views are rated the most severely.
Highlights
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The influence of orthodontic bracket prescription on smile attractiveness and according to the sagittal skeletal pattern was evaluated in this cross-sectional study.
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The attractiveness of a smile is affected by the prescription of the brackets.
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Different views of the same smile do not get similar esthetic scores.
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The specialty of the evaluators influences the esthetic rating of the smiles, contrary to their gender and age.
An esthetically pleasing smile is the key feature of facial appearance. Dentoalveolar esthetics remains an essential aspect of esthetic dentistry, particularly in the segment extending between the maxillary first premolars, also known as the esthetic zone. , The anteroposterior inclination of the anterior teeth, as well as their mesiodistal orientation, affect both the 3-dimensional (3D) control of occlusion and the perception of the smile in profile and frontal views. The labiolingual inclination of the posterior teeth also plays an important role in the esthetics of a smile. ,
In 1972, Andrews described the 6 keys to normal occlusion, in which he presented the notion of tooth inclination and set ideal standards on the matter. He then introduced a system of preinformed orthodontic brackets in which he incorporated first-, second-, and third-order information. His innovations inspired many orthodontists, such as Roth, Ricketts, McLaughlin, Bennett, and Trevisi, who later developed their techniques and prescriptions.
In clinical practice, patients often present with sagittal skeletal discrepancies. In certain situations, dentoalveolar compensations should be applied to overcome these skeletal disharmonies. , Nowadays, the main objective of orthodontic treatment should be to improve dentofacial esthetics rather than to focus exclusively on occlusal relationships. It would thus be interesting to define the esthetic limits of dental compensation in patients with maxillomandibular discrepancies, especially in the sagittal direction.
Former research on dentoalveolar esthetics has mainly focused on modifications in the position of a single tooth or a limited number of teeth. , , , Moreover, these modifications consist mostly of digital alterations on photographic images and a bidimensional plane. In reality, all the bonded teeth are positioned in a 3D spatial configuration depending on the information introduced in the orthodontic brackets, which can then influence the esthetic perception of a smile. In this sense, the OrthoAnalyzer software (3Shape, Copenhagen, Denmark) offers the advantage of containing a library with different orthodontic bracket systems and prescriptions that can be used to simulate the final 3D position of the teeth after orthodontic treatment.
To our knowledge, no study has evaluated the effect of various bracket prescriptions on the appearance of a smile, especially when considering the sagittal skeletal classification. Therefore, the main objective of this study was to evaluate the influence of orthodontic bracket prescription on the evaluation of the attractiveness of a smile while using the OrthoAnalyzer software.
Clinically, it is essential for a practitioner to appreciate a smile from multiple perspectives, including frontal, oblique, and sagittal views. , , Similarly, it appears to be essential to extend our esthetic research in these 3 dimensions to reach valid conclusions. The secondary objectives of this study were to evaluate the influence of orthodontic prescription on smile attractiveness according to the sagittal skeletal pattern, to evaluate the effect of the specialty and gender of the evaluators on the perception of smile esthetics, and to assess whether the frontal, oblique and sagittal views of the same smile obtain similar esthetic scores.
Material and methods
This cross-sectional study has received the agreement of the research ethics committee of the Saint Joseph University of Beirut (USJ-2022-203).
The following protocol was applied. Three female patients, previously treated at the Dental Care Center of the Saint Joseph University of Beirut, were chosen according to their sagittal skeletal pattern: 1 patient with a skeletal Class I malocclusion with normal incisors relationships, 1 patient with compensated skeletal Class II malocclusion, and 1 patient with compensated skeletal Class III malocclusion. Their selection was also based on the following criteria: adult patient having signed the informed consent with a normodivergent facial pattern, a centered dental midline, Class I dental relationships, a gingival exposure at smile <3 mm, a healthy periodontium, a maxillary dental arch not presenting crowding or irregularities, decayed or fractured teeth, temporary teeth, dental agenesis or extracted teeth, anomalies of shape or size, and without a history of orthognathic surgery. Lateral cephalometric examinations were used to diagnose the sagittal skeletal pattern, according to the Tweed and Wits analyses: skeletal Class I malocclusion was confirmed by the presence of an ANB angle between 1° and 5° and an AoBo measurement of 0 mm for women, skeletal Class II malocclusion presented an ANB angle superior to 5° and an AoBo measurement superior to 2 mm for women, and skeletal Class III malocclusion presented an ANB angle inferior to 1° and an AoBo measurement inferior to −2 mm for women. ,
For each patient, 3 points were marked on the gingiva with an indelible marker: point A, located between the right canine and lateral incisor; point B, located between the right central and lateral incisors; and point C, located between the left central and lateral incisors. Three smiling photographs were taken with the subject’s head horizontal, at a distance of 1.5 m from the camera (Canon EOS 800D; Ota City, Tokyo, Japan), at a speed of 8 milliseconds with flash and an aperture of F8, under standard lighting conditions, with a black background, and in 3 different views: anterior 0° smile, oblique 45° smile, and lateral 90° smile ( Fig 1 ).
The maxillary arch was then directly scanned using 3Shape Trios (3Shape) to preserve the clarity of the marked points, and the optical impression was transferred to the OrthoAnalyzer software in which a 3D digital maxillary model was constructed. The model was segmented, and the “Bracket Placement” option was chosen. FA point, being the center of the clinical crown as described by Andrews, was confirmed for each tooth, and the most appropriate arch shape was selected ( Fig 2 ). The virtual brackets with 0.022 × 0.028-in dimensions were chosen from the OrthoAnalyzer software library and then ideally positioned on 10 maxillary teeth, from the second right premolar to the second left premolar, with respect to the dental axis and the FA point. A rectangular archwire with a section of 0.019 × 0.025-in was selected, then the information of the virtual brackets was expressed for 3 prescription types: Roth (U/L 5 × 5 Victory Series, Roth 0.022-in; 3M, Unitek, Monrovia, Calif), MBT (U/L 5 × 5 Victory Series, MBT 0.022-in; 3M Unitek) and Ricketts (U/L 5 × 5 APC Flash-Free SmartClip, Ricketts 0.022-in; 3M Unitek) ( Table I ). The simulated position of the maxillary teeth was then captured in 3 views: 0°, 45°, and 90° ( Fig 3 ).
| Teeth no. | 11/21 | 12/22 | 13/23 | 14/24 | 15/25 |
|---|---|---|---|---|---|
| Roth | |||||
| Third order (°) | 12 | 8 | −2 | −7 | −7 |
| Second order (°) | 5 | 9 | 13 | 0 | 0 |
| MBT | |||||
| Third order (°) | 17 | 10 | −7 | −7 | −7 |
| Second order (°) | 5 | 9 | 11 | 2 | 2 |
| Ricketts | |||||
| Third order (°) | 22 | 14 | 7 | 0 | 0 |
| Second order (°) | 0 | 8 | 5 | 0 | 0 |
To fabricate the smile images to be evaluated, the original photographs were first uploaded to the Adobe Photoshop software (version 8.0, Adobe Photoshop CS; Adobe Systems Inc, San Jose, Calif), and the initial maxillary teeth were removed from the images. To reduce bias that could be encountered during the judgment, the photographs were subsequently cropped, maintaining a frame extending from the cutaneous suborbital point to below cutaneous point B. Second, the digital scans were uploaded to the OrthoAnalyzer software. The options “2D measurements” and “Image Overlay” were successively selected, and the edited photographs of the patient were uploaded. The option “Scale 1:1” was then selected to obtain a real size ratio of the teeth. The previously marked points served as a reference for superimposing the digital model on the uploaded photograph. The superimposition of the sagittal and oblique views was done on the A and B points, and the superimposition of the frontal view was done on points A and C. Finally, the obtained superimpositions were uploaded in the Adobe Photoshop software, and the Photoshop expert modified the texture and color of the digital teeth into a more realist appearance. This process was repeated for the 3 patients until a total of 27 smile images were obtained ( Figs 4-6 ).
For data acquisition, a web-based “Google Forms” survey was used. For the assessment of the scoring reliability, 3 images were duplicated in the survey. The visual analog scale (0-10) was used to rate the different images (0, a very unattractive smile; 10, a very attractive smile). No specific information related to the images was shared with the evaluators, except that the subjects were females, and they were asked to rate the attractiveness of the smiles. The judges viewed all the images first and then assigned their scores. , , At the beginning of the survey, each evaluator mentioned their age in the number of years, their gender, and their specialty.
A power analysis was performed a priori to determine the sample size, using G∗ power software for analysis of variance (ANOVA) with repeated measures and taking into consideration a power of 80%, an α error of 5%, a correlation coefficient of 0.5, and a small effect size of 0.1. The minimum number of required evaluators was 165. The sample was then divided into 3 groups according to their specialty: orthodontists, dentists, and laypeople, and into 2 groups according to their gender. The dentists and orthodontists have completed their professional training. The laypeople consisted of adults aged >18 years and unrelated to dentistry.
Statistical analysis
Data analysis was performed using SPSS software (version 26; IBM, Armonk, NY). Descriptive statistics of quantitative and qualitative variables were presented respectively as mean ± standard deviation and frequencies. Intraobserver reproducibility was assessed using the estimation of the intraclass correlation coefficient and its 95% confidence interval for a 2-factor mixed-effects model, a single evaluator, and absolute agreement (<0.50, poor; 0.50-0.75, fair; 0.75-0.90, good; >0.9, excellent reproducibility).
For the comparison of 3 dependent means, the repeated measures ANOVA was used, followed by Bonferroni’s post-hoc test for multiple comparisons. The homogeneity of the variances was verified using Levene’s test. For the comparison of 2 independent means, the Student t test was used, and for the comparison of 3 independent means, the following statistical tests were used: Welch’s ANOVA followed by Games-Howell’s post-hoc test when the hypothesis of the homogeneity of variances was rejected and univariate ANOVA when variances were homogeneous, followed by Bonferroni adjustment for multiple comparisons. A generalized estimating equation (GEE) model was finally carried out to determine the predictors of the variation of esthetic scores; adjusted odds ratios and their 95% confidence intervals were calculated and reported. The significance level was set at 5%, and all tests were 2-sided.
Results
This study included 167 evaluators, with a mean age of 38.47 ± 14.15 years (minimum, 20 years; maximum, 70 years). The evaluators were distributed, according to gender, between 71 males (42.5%) and 96 females (57.5%) and, according to their specialty, among 54 orthodontists (32.2%), 54 dentists (32.2%), and 59 laypersons (35.3%).
The results of the intraobserver reproducibility are shown in Table II . The judge’s reliability scores were moderate to well, with a 95% confidence level.
| Variables | ICC | 95% CI | P value |
|---|---|---|---|
| Skeletal Class I (Ricketts), frontal view | 0.724 | 0.643-0.789 | <0.001 ∗ |
| Skeletal Class II (MBT), sagittal view | 0.756 | 0.683-0.815 | <0.001 ∗ |
| Skeletal Class III (Roth), oblique view | 0.646 | 0.547-0.726 | <0.001 ∗ |
The mean scores assigned to the smiling images, according to bracket prescription, skeletal Class, and view, regardless of the specialty and gender of the evaluators, are shown in Table III .
| View | Prescription | P value | ||
|---|---|---|---|---|
| Roth | Ricketts | MBT | ||
| Skeletal Class I | ||||
| Frontal | 6.47 ± 1.86 a | 5.77 ± 1.91 b | 6.14 ± 1.82 a | <0.001 ∗ |
| Oblique | 6.47 ± 1.81 a | 5.80 ± 2.02 b | 6.56 ± 1.83 a | <0.001 ∗ |
| Lateral | 6.38 ± 1.76 a | 5.30 ± 1.85 c | 6.13 ± 1.86 b | <0.001 ∗ |
| Skeletal Class II | ||||
| Frontal | 5.71 ± 1.95 | 5.50 ± 2.04 | 5.70 ± 2.11 | 0.286 |
| Oblique | 5.54 ± 1.74 a | 4.80 ± 2.08 b | 5.43 ± 1.85 a | <0.001 ∗ |
| Lateral | 5.32 ± 2.07 a | 3.79 ± 2.03 c | 4.98 ± 2.15 b | <0.001 ∗ |
| Skeletal Class III | ||||
| Frontal | 5.49 ± 1.92 a | 5.29 ± 1.96 ab | 5.18 ± 1.99 b | 0.044 ∗ |
| Oblique | 5.72 ± 1.87 a | 4.29 ± 2.12 c | 5.03 ± 1.88 b | <0.001 ∗ |
| Lateral | 5.22 ± 1.95 a | 4.66 ± 2.05 b | 5.08 ± 2.09 a | <0.001 ∗ |
The tests results for the frontal view smiling images are described as follows. For the patient with skeletal Class I malocclusion, statistically significant differences were found between the esthetic scores when comparing Ricketts to Roth and Ricketts to MBT, with the Roth prescription receiving the highest scores (6.47 ± 1.86) and the Ricketts prescription receiving the lowest scores (5.77 ± 1.91). For the patient with skeletal Class II malocclusion, no statistically significant difference was found between the 3 prescriptions ( P = 0.286). For the patient with skeletal Class III malocclusion, significant differences were only observed between the Roth and MBT prescriptions, with the highest scores attributed to Roth (5.49 ± 10.92) and the lowest scores attributed to MBT (5.18 ± 1.99).
The tests results for the oblique view smiling images are described as follows. For the patients with skeletal Class I and II malocclusions, statistically significant differences were found between the esthetic scores when comparing Ricketts to Roth and Ricketts to MBT, with the Ricketts prescription receiving the lowest mean scores: 5.80 ± 2.02 for the patient with skeletal Class I malocclusion and 4.80 ± 2.08 for the patient with skeletal Class II malocclusion. The highest scores were attributed to the MBT prescription for the patient with skeletal Class I malocclusion (6.56 ± 1.83), and to the Roth prescription for the patient with skeletal Class II malocclusion (5.54 ± 1.74). For the patient with skeletal Class III malocclusion, significant differences were noted between the 3 prescriptions, with the Roth prescription receiving the highest scores (5.72 ± 1.87) and the Ricketts prescription receiving the lowest scores (4.29 ± 2.12).
The tests results for the lateral view smiling images are described as follows. Statistically significant differences in the scores were observed between the 3 prescriptions for the 3 patients, except between the Roth and MBT prescriptions for the patient with skeletal Class III malocclusion. In all 3 patients, the Roth prescription received the highest scores (Class I, 6.38 ± 1.76; Class II, 5.32 ± 2.07; Class III, 5.22 ± 1.95), whereas the Ricketts prescription received the lowest scores (Class I, 5.30 ± 1.85; Class II, 3.79 ± 2.03; Class III, 4.66 ± 2.05).
The mean scores assigned by the 3 specialty groups to the smiling images, according to bracket prescription, skeletal Class, and view, regardless of the age of the evaluators, are shown in Table IV .
| Variables | Specialty | P value | ||
|---|---|---|---|---|
| Orthodontists (n = 54) | Dentists (n = 54) | Laypeople (n = 59) | ||
| Skeletal Class I | ||||
| Frontal view | ||||
| Roth | 6.76 ± 1.69 a | 6.15 ± 1.95 a | 6.49 ± 1.91 | 0.232 |
| Ricketts | 5.91 ± 1.81 b | 5.33 ± 1.83 b | 6.03 ± 2.02 | 0.121 |
| MBT | 6.37 ± 1.62 ab | 5.74 ± 1.92 ab | 6.31 ± 1.86 | 0.138 |
| P value | <0.001 ∗ | 0.016 ∗ | 0.065 | |
| Oblique view | ||||
| Roth | 6.80 ± 1.69 a | 6.00 ± 2.06 ab | 6.61 ± 1.61 a | 0.055 |
| Ricketts | 5.85 ± 1.94 b | 5.46 ± 2.14 b | 6.07 ± 1.96 b | 0.277 |
| MBT | 6.89 ± 1.72 a | 6.46 ± 2.02 a | 6.34 ± 1.75 ab | 0.255 |
| P value | <0.001 ∗ | 0.001 ∗ | 0.036 ∗ | |
| Sagittal view | ||||
| Roth | 6.81 ± 1.66 a | 6.13 ± 1.89 a | 6.20 ± 1.67 a | 0.082 |
| Ricketts | 5.61 ± 1.61 b | 4.85 ± 1.98 b | 5.42 ± 1.89 b | 0.083 |
| MBT | 6.72 ± 1.65 Aa | 5.91 ± 2.07 ABa | 5.78 ± 1.73 Bb | 0.015 ∗ |
| P value | <0.001 ∗ | <0.001 ∗ | <0.001 ∗ | |
| Skeletal Class II | ||||
| Frontal view | ||||
| Roth | 5.65 ± 1.85 | 5.39 ± 2.07 | 6.07 ± 1.90 a | 0.174 |
| Ricketts | 5.76 ± 1.83 | 5.24 ± 1.99 | 5.51 ± 2.27 b | 0.422 |
| MBT | 5.87 ± 2.04 A | 4.72 ± 2.10 B | 6.44 ± 1.87 Aa | <0.001 ∗ |
| P value | 0.681 | 0.070 | <0.001 ∗ | |
| Oblique view | ||||
| Roth | 5.65 ± 1.86 ABa | 5.04 ± 1.52 B | 5.92 ± 1.74 Aa | 0.023 ∗ |
| Ricketts | 4.76 ± 2.34 b | 4.56 ± 2.14 | 5.07 ± 1.76 b | 0.373 |
| MBT | 5.89 ± 1.85 a | 5.11 ± 1.63 | 5.31 ± 1.98 b | 0.074 |
| P value | <0.001 ∗ | 0.059 | <0.001 ∗ | |
| Sagittal view | ||||
| Roth | 5.59 ± 1.83 a | 5.85 ± 2.02 a | 5.51 ± 2.26 a | 0.122 |
| Ricketts | 4.22 ± 1.92 Ab | 3.11 ± 1.91 Bc | 4.02 ± 2.12 Ab | 0.009 ∗ |
| MBT | 5.67 ± 1.91 Aa | 3.93 ± 1.98 Bb | 5.31 ± 2.18 Aa | <0.001 ∗ |
| P value | <0.001 ∗ | <0.001 ∗ | <0.001 ∗ | |
| Skeletal Class III | ||||
| Frontal view | ||||
| Roth | 5.87 ± 2.16 | 5.31 ± 1.68 a | 5.31 ± 1.88 | 0.212 |
| Ricketts | 5.56 ± 2.19 AB | 4.67 ± 1.78 Bb | 5.63 ± 1.77 A | 0.016 ∗ |
| MBT | 5.46 ± 2.08 | 4.85 ± 1.81 ab | 5.22 ± 2.05 | 0.276 |
| P value | 0.113 | 0.016 ∗ | 0.124 | |
| Oblique view | ||||
| Roth | 5.93 ± 2.01 a | 5.33 ± 1.80 a | 5.88 ± 1.76 a | 0.182 |
| Ricketts | 4.24 ± 2.20 b | 4.00 ± 2.06 b | 4.61 ± 2.09 b | 0.305 |
| MBT | 5.41 ± 2.04 a | 4.85 ± 1.58 a | 4.85 ± 1.95 b | 0.200 |
| P value | <0.001 ∗ | <0.001 ∗ | <0.001 ∗ | |
| Sagittal view | ||||
| Roth | 5.31 ± 2.01 AB | 4.70 ± 1.81 Ba | 5.61 ± 1.95 Aa | 0.042 ∗ |
| Ricketts | 5.17 ± 1.95 A | 4.09 ± 1.70 Bb | 4.73 ± 2.31 ABb | 0.010 ∗ |
| MBT | 5.48 ± 2.09 | 4.70 ± 1.99 a | 5.05 ± 2.16 b | 0.154 |
| P value | 0.449 | 0.014 ∗ | <0.001 ∗ | |
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