Evaluation of orthodontic treatment need awareness in individuals with different malocclusions

Abstract

Background

This study aimed to evaluate the associations among malocclusion awareness, aesthetic perception, and motivations for seeking orthodontic care in relation to demographic variables.

Methods

A total of 180 individuals aged 13–35 years with no prior history of orthodontic treatment were included in this cross-sectional study. Awareness of orthodontic treatment need was assessed using the Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN-AC) and a 5-point Likert scale. Reasons for seeking care, referral patterns, and sources of orthodontic information were recorded. Data were analyzed using non-parametric tests and Spearman’s correlation analysis (p < 0.05).

Results

A strong negative correlation was identified between IOTN-AC scores and smile satisfaction (r = -0.661; p < 0.05). Significant differences were observed among malocclusion classes, with the highest scores recorded in individuals with Class III malocclusion (p < 0.05). No significant differences were found with respect to sex or age groups (p > 0.05). The majority of participants reported dissatisfaction with their smile. Significant associations were also observed between reasons for seeking care and both malocclusion type and age.

Conclusion

Aesthetic perception is a key determinant of orthodontic treatment demand and is closely associated with malocclusion type. Therefore, patients’ aesthetic concerns and awareness should be considered during orthodontic evaluation and treatment planning to optimize patient-centered outcomes.

Clinical trials number

Not applicable.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12903-026-09165-x.

Introduction

Orthodontics is a specialized field within dentistry concerned with the diagnosis, prevention, and management of malocclusions, as well as the improvement of functional and structural relationships within the dentofacial complex [1]. The objectives of orthodontic treatment extend beyond achieving optimal occlusion to include the establishment of functional harmony and dentofacial aesthetic balance [1]. Malocclusions are common clinical conditions that may compromise this balance, leading not only to functional impairment but also to aesthetic concerns and psychosocial consequences [2, 3].

Dentofacial aesthetics play a significant role in individuals’ self-perception and health-related quality of life. Increased social interaction and heightened societal emphasis on physical appearance—particularly during adolescence—have been associated with greater awareness of dentofacial aesthetics [4]. Moreover, increasing access to internet-based information sources and online communication environments has enhanced awareness of facial harmony, symmetry, and idealized appearance, potentially facilitating recognition of orthodontic irregularities [5]. Consequently, malocclusions should be considered not only as functional disturbances but also as conditions with substantial psychosocial implications, including reduced self-esteem, social inhibition, and impaired psychological well-being.

Early detection of orthodontic anomalies and timely referral for treatment have been shown to improve treatment outcomes, enhance functional and aesthetic results, and reduce adverse psychosocial effects [6]. In addition, patient expectations regarding facial aesthetics and perceived treatment outcomes are among the primary determinants of motivation for seeking orthodontic care [7]. Individual awareness of orthodontic treatment need has also been shown to influence treatment engagement and overall satisfaction. Demographic factors such as age, sex, and educational level may further affect awareness and perception. Therefore, evaluating the relationship between subjective perception of malocclusion and objective clinical findings is essential for accurate diagnosis, effective treatment planning, and improved patient-centered outcomes [8, 9].

Various orthodontic indices have been developed to provide an objective assessment of malocclusion severity and treatment need [10, 11]. Previous studies have demonstrated that factors such as age, education, malocclusion type, and geographic location influence individuals’ awareness and perception of the need for orthodontic treatment. In particular, geographic variability plays a significant role in shaping aesthetic perception and levels of awareness [8, 12]. However, despite the growing body of evidence, limited studies have comprehensively evaluated the combined influence of malocclusion awareness, aesthetic perception, and treatment motivation within specific populations. Furthermore, the relationships between malocclusion awareness, referral patterns, and sources of orthodontic knowledge have received limited attention in the literature. Understanding how these factors interact may provide valuable insight into the development of orthodontic treatment demand and patient-centered treatment planning.

Therefore, the aim of the present study was to evaluate the associations among malocclusion awareness, dentofacial aesthetic perception, and motivations for seeking orthodontic care in treatment-seeking individuals, with reference to demographic variables, using the IOTN-AC and a standardized Likert scale.

Methods

Study design and ethical considerations

This cross-sectional study included 180 individuals (102 females, 56.7%; 78 males, 43.3%) aged 13–35 years who presented to the Department of Orthodontics, Faculty of Dentistry, Mersin University, between September 30 and December 30, 2024. Ethical approval was obtained from the Mersin University Clinical Research Ethics Committee (Approval No: 2024/637). The study protocol was conducted in accordance with the principles of the Declaration of Helsinki, and prior to the procedures, written informed consent was obtained from all participants and from the parents and/or legal guardians of minors.

Sample size determination and eligibility criteria

Sample size calculation was performed using G*Power software (Version 3.1.9.6) based on the results of a previous study with a similar methodology comparing different malocclusion types. With a 95% confidence level (1 − α), 80% statistical power (1 − β), and an effect size of f = 0.54, the minimum required sample size was calculated as 162 participants [13].

Inclusion criteria comprised the absence of prior orthodontic treatment, no history of systemic disease, willingness to complete the questionnaire, and written informed consent. Exclusion criteria included age outside the specified range, prior orthodontic treatment, presence of systemic disease, and incomplete consent or questionnaire data. The age groups were defined according to developmental stages rather than equal chronological intervals.

Participant characteristics and malocclusion classification

Participants were stratified into three age groups: 13–18, 19–24, and 25–35 years. The age range of 13–35 years was selected because adolescents and young adults constitute the primary population seeking orthodontic treatment and represent the age groups most commonly evaluated in studies investigating orthodontic awareness, treatment motivation, and aesthetic perception [8, 9]. Individuals younger than 13 years were excluded due to the potential influence of parental guidance on treatment awareness and self-perception, whereas individuals older than 35 years were excluded to reduce the potential impact of age-related dental and periodontal changes on aesthetic perception and treatment-seeking behavior. All individuals were informed about the study procedures, and participation was voluntary. Malocclusion classification was based on clinical examination and dental occlusal relationships recorded at the initial visit. Cephalometric analyses were not incorporated into the study protocol, as the primary objective was to evaluate patients’ awareness, aesthetic perception, and treatment-seeking behavior according to clinically identified malocclusion categories.

Data collection and questionnaire assessment

Data collection was performed using a structured questionnaire adapted from the study by Topal [8] and modified according to the objectives of the present study. The final questionnaire consisted of three sections. The first section collected demographic information, including age and sex. The second section included ten standardized intraoral photographs representing the IOTN-AC. Participants were instructed to select the image that most closely resembled their dentofacial appearance and to evaluate their malocclusion and smile satisfaction using a Likert scale. The third section assessed participants’ primary reasons for seeking orthodontic care, referral sources, and the source from which they first obtained information about orthodontics. The English version of the modified questionnaire is provided as Supplementary File 1. Questionnaires were reviewed immediately after completion. Participants with incomplete questionnaire responses were excluded from the study, and no missing data were present in the final dataset included in the analyses.

Assessment of orthodontic treatment need

The IOTN-AC, a validated 10-point visual scale, was used to assess the aesthetic component of malocclusion (Fig. 1) [14, 15]. A score of 1 represents the most favorable dental aesthetics, whereas a score of 10 indicates the least favorable [16, 17]. Participants selected the image that corresponded to their self-perception without using a mirror or external assistance. This approach was adopted to capture participants’ spontaneous self-perception and to minimize the influence of real-time visual comparison, which could potentially alter subjective aesthetic judgments. Similar approaches have been used in previous studies evaluating self-perceived orthodontic treatment need and aesthetic awareness [8].

Fig. 1.

Photographic scale of the IOTN-AC component (adapted from Brook and Shaw [16])

Following image selection, self-perceived smile satisfaction was evaluated using a standardized 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied). To minimize response bias, reverse coding was applied to two items. Participants’ primary presenting complaints, referral patterns, and initial sources of orthodontic knowledge were systematically recorded. All clinical evaluations and malocclusion classifications were performed by the same investigator under standardized clinical conditions.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA). The normality of data distribution was assessed using the Shapiro–Wilk test. As the data were not normally distributed, nonparametric tests were employed. Comparisons of continuous variables were conducted using the Kruskal–Wallis test, followed by pairwise comparisons with the Mann–Whitney U test. Given the multicategorical structure of the contingency tables, Pearson’s chi-square test was used to evaluate overall associations between categorical variables. For contingency tables with low expected cell frequencies, the Monte Carlo exact test (Fisher–Freeman–Halton extension of Fisher’s Exact Test, 2,000,000 replicates) was additionally performed to evaluate the overall association, as the exact computational algorithm exceeded available workspace for these large, sparse tables. Correlations between variables were evaluated using Spearman’s rank correlation coefficient. A p-value of < 0.05 was considered statistically significant.

Results

A total of 180 individuals were included (102 females, 56.7%; 78 males, 43.3%). Participants were evenly distributed across three age groups (13–18, 19–24, and 25–35 years), with 60 individuals (33.3%) in each group. The mean age was 21.8 ± 6.4 years.

The distribution of IOTN-AC scores according to sex, age group, and malocclusion type is presented in Table 1. No statistically significant differences were observed with respect to sex or age group (p > 0.05), whereas a significant difference was found among malocclusion groups (p < 0.05). The highest IOTN-AC scores were observed in individuals with Class III malocclusion, while the lowest scores were recorded in those with Class I malocclusion.

Table 1.

Mean IOTN-AC scores by sex, age group, and malocclusion type

		IOTN-AC
		Min	Mean ± SD	Max
Sex	Male (n = 78)	1	5.01 ± 2.41	10
	Female (n = 102)	1	4.50 ± 2.52	10
		p = 0.102*
		IOTN-AC
		Min	Mean ± SD	Max
Age	13–18 (n = 60)	1	5.18 ± 2.53	10
	19–24 (n = 60)	1	4.25 ± 2.28	10
	25–35 (n = 60)	1	4.73 ± 2.57	10
		p = 0.153**
		IOTN-AC
		Min	Mean ± SD	Max
Malocclusion	Class I (n = 6)	1	3.00 ± 2.90	8
	Class II (n = 125)	1	4.43 ± 2.25	10
	Class II subdiv. (n = 14)	1	3.64 ± 2.17	8
	Class III (n = 35)	1	6.49 ± 2.53	10
		p < 0.001**

*Mann-Whitney-U, **Kruskal Wallis

n Number of individuals, SD Standard deviation, Min Minimum value, Max Maximum value

Participants’ levels of satisfaction with their smile are summarized in Table 2. Overall, 21.1% of participants reported being “not satisfied at all,” 40.6% “not satisfied,” 30.0% “neutral,” 6.7% “satisfied,” and 1.7% “very satisfied.”

Table 2.

Smile satisfaction levels of participants based on the Likert scale

Level of Satisfaction	Number of Individuals (n)	Percentage distribution (%)
Not satisfied at all	38	21.11
Not satisfied	73	40.57
Neutral	54	30
Satisfied	12	6.66
Very satisfied	3	1.66
Total	180	100

The associations between reasons for seeking orthodontic care, malocclusion type, and age groups are presented in Table 3. According to malocclusion type, complaints related to anteroposterior maxillomandibular discrepancy, transverse maxillomandibular discrepancy, and cleft lip and palate (CLP) differed significantly among groups (p < 0.05). Individuals with anteroposterior discrepancies were predominantly those with Class II malocclusion, whereas transverse discrepancies and CLP were more frequently observed in individuals with Class III malocclusion. Analysis of reasons for seeking care across age groups revealed statistically significant differences for most variables (p < 0.05), except for anteroposterior discrepancy, mouth breathing, oral habits (e.g., thumb sucking, tongue thrusting), and overbite/overjet anomalies. In the 13–18 age group, the most common reasons for presentation were crowding/misalignment, interdental spacing (diastema), and CLP, whereas transverse discrepancies and impacted teeth were reported least frequently. In contrast, presentations due to impacted teeth and posterior crossbite were more common in the 25–35 age group. The overall association was further confirmed by Monte Carlo exact testing (Fisher–Freeman–Halton extension); however, the specific subgroup patterns identified through adjusted residuals should be interpreted with caution given the sparse cell counts in several categories.

Table 3.

Association between reasons for seeking care, malocclusion type, and age groups

Chief complaint		Crowding / Misalignment	Interdental spacing / Diastema	Anteroposterior maxillomandibular discrepancy	Transverse maxillomandibular discrepancy	Mouth breathing, thumb sucking, tongue thrusting and related oral habits	Impacted teeth	Deep bite / open bite	Posterior crossbite	Temporomandibular joint disorder	Cleft lip and palate
Malocclusion	Class I	3	2	0	0	0	0	1	0	0	0
	Class II	31	24	29	9	2	20	3	4	3	0
	Class II Subdiv.	6	2	0	1	0	3	1	1	0	0
	Class III	5	6	2	10	0	4	1	4	0	3
	p*	> 0.05	> 0.05	0.003*	0.003*	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	0.003*
Age	13–18	22	15	11	2	0	4	2	1	0	3
	19–24	17	14	6	9	0	8	2	1	3	0
	25–35	6	5	14	9	2	15	2	7	0	0
	p*	< 0.001*	< 0.001*	> 0.05	< 0.001*	> 0.05	< 0.001*	> 0.05	< 0.001*	< 0.001*	< 0.001*

*Chi-square test; overall associations were confirmed by Monte Carlo exact testing (Fisher–Freeman–Halton extension): chief complaint–malocclusion type, p=0.0016; chief complaint–age group, p<0.001. Cell-specific comparisons are based on adjusted residuals

The association between referral source and malocclusion type is presented in Table 4. A significant overall association between referral source and malocclusion type was observed. However, category-specific differences should be interpreted cautiously because several cells contained low frequencies. A higher prevalence of Class II malocclusion was noted among individuals referred by dentists as well as those who were self-referred. The overall association was further confirmed by Monte Carlo exact testing (Fisher–Freeman–Halton extension); however, category-specific differences should be interpreted with caution given the sparse cell counts in several categories.

Table 4.

Association between referring source and malocclusion type

Referring source	Malocclusion				p*
	Class I	Class II	Class II Subdiv.	Class III
Self-referral	4	49	2	17	0.004*
Dentist	2	51	5	7	0.004*
Other	0	6	3	9	0.004*
Family	0	17	4	1	> 0.05
Friends	0	2	0	1	> 0.05

*Chi-square test; the overall association was confirmed by Monte Carlo exact testing (Fisher–Freeman–Halton extension, p=0.0016). Cell-specific comparisons are based on adjusted residuals

The relationships between sources of orthodontic knowledge, referral source, and age groups are presented in Table 5. A statistically significant association was observed between information source and referral source (p < 0.05). Among self-referred individuals, the most common source of information was the internet, whereas individuals referred by dentists primarily obtained information from dental professionals. When analyzed by age group, a significant association between information source and age was observed in the 13–18 and 25–35 age groups (p < 0.05). The primary source of information was dentists in the 13–18 age group, while the internet was predominant in the 25–35 age group.

Table 5.

Association between information source, referring source, and age group

		Source of Orthodontic Knowledge					p*
		Television	Family	Dentist	Internet	Friends
Referring source	Self-referral	7	3	10	45	7	< 0.001*
	Dentist	1	0	44	16	4	< 0.001*
	Other	3	2	4	8	1	< 0.001*
	Family	0	10	7	5	0	< 0.001*
	Friends	0	0	0	0	3	< 0.001*
Age	13–18	0	9	30	14	7	< 0.001*
	19–24	3	5	23	26	3	> 0.05
	25–35	8	1	12	34	5	< 0.001*

*Chi-square test

The results of the Spearman’s correlation analysis are presented in Table 6. A statistically significant positive correlation was observed between age and smile satisfaction (r = 0.261, p < 0.01), indicating that smile satisfaction tended to increase with age. In contrast, a strong negative correlation was found between aesthetic score and smile satisfaction (r = − 0.661, p < 0.01), suggesting that individuals with higher perceived aesthetic impairment reported lower levels of smile satisfaction. No statistically significant correlation was identified between age and aesthetic score (r = − 0.115, p > 0.05).

Table 6.

Spearman’s correlation analysis of age, aesthetic score, and smile satisfaction

Variables	Age	Aesthetic Score	Satisfaction
Age	1.000	-0.115	0.261**
Aesthetic Score	-0.115	1.000	-0.661**
Satisfaction	0.261**	-0.661**	1.000

Values represent Spearman’s rho correlation coefficients

p < 0.01 (two-tailed) is indicated by **

N = 180 for all analyses

Discussion

Malocclusions are commonly assessed using standardized orthodontic indices that incorporate both aesthetic and functional parameters. While these indices provide objective measures of treatment need, patients’ perceptions remain inherently subjective and may not always align with clinical findings. Variations in dentofacial awareness may lead to differences in the perception of similar malocclusions. This highlights the importance of integrating patient expectations and chief complaints into orthodontic diagnosis and treatment planning. Previous studies have demonstrated that patients with greater awareness of their orthodontic needs and clearly defined expectations tend to show better treatment compliance and satisfaction [18]. Therefore, effective clinician–patient communication and patient-centered treatment planning are essential for optimal clinical outcomes [19].

No significant differences in IOTN-AC scores were observed with respect to sex or age. While some studies have reported higher aesthetic awareness among females, others have found no sex-related differences, suggesting that aesthetic perception is influenced by multiple factors beyond biological sex [20]. Similarly, the absence of age-related differences may be explained by the stabilization of aesthetic perception during adolescence and early adulthood, as well as shared sociocultural influences within similar age groups [21]. These findings indicate that dentofacial aesthetic perception is shaped by multifactorial determinants rather than isolated demographic variables.

A significant association was identified between malocclusion type and IOTN-AC scores. Individuals with Class III malocclusion exhibited the highest aesthetic scores, whereas those with Class I malocclusion showed the lowest. These findings suggest that awareness of orthodontic treatment need may vary according to the type of malocclusion.

The higher aesthetic awareness observed among individuals with Class III malocclusion may reflect the more noticeable clinical presentation of this malocclusion. However, as cephalometric skeletal relationships were not evaluated in the present study, any interpretation based on underlying skeletal characteristics would be speculative. Therefore, the present findings should be interpreted with reference to clinically identified malocclusion categories rather than skeletal morphology [22, 23]. In contrast, the relatively mild presentation of Class I malocclusion may limit its perceived aesthetic impact.

The comparatively lower awareness observed in Class II malocclusion may be related to its often less noticeable effect on the facial profile, particularly in predominantly dentoalveolar cases. Moreover, the higher prevalence of Class II malocclusion in the general population may contribute to normalization and reduced perceived severity. Nevertheless, previous studies have reported considerable variability in aesthetic perception among individuals with similar malocclusion characteristics, suggesting that malocclusion type alone does not fully explain dentofacial aesthetic awareness [24].

The strong negative correlation between aesthetic score and smile satisfaction (r = − 0.661) indicates that individuals with higher perceived aesthetic impairment reported lower levels of smile satisfaction. This finding supports previous studies showing that aesthetic concerns are among the main factors driving orthodontic treatment demand [20, 25, 26]. A weak but significant positive correlation was also observed between age and smile satisfaction (r = 0.261, p < 0.01), suggesting that older individuals tended to be more satisfied with their smile. However, no significant correlation was found between age and aesthetic score, indicating that perceived malocclusion severity did not differ substantially across age groups. Together, these findings suggest that while awareness of malocclusion remains relatively stable with age, satisfaction with dentofacial appearance may vary among different age groups.

The predominance of Class III malocclusion among individuals reporting transverse discrepancies and facial asymmetry may reflect the greater clinical visibility of these characteristics, which could increase awareness of orthodontic problems and treatment need [1, 27]. Similarly, individuals with cleft lip and palate frequently present with complex dentofacial anomalies that may contribute to increased aesthetic concern and treatment demand [28, 29]. However, because cephalometric assessment was not performed, these findings should be interpreted according to clinically identified malocclusion categories rather than underlying skeletal characteristics.

Age-related differences in reasons for seeking care were also observed. Adolescents more frequently reported concerns related to anterior aesthetics, such as crowding and diastema, consistent with previous findings highlighting the importance of dental appearance during this developmental period [30, 31]. In contrast, impacted teeth were more commonly reported among older individuals, likely due to delayed diagnosis and the asymptomatic nature of early impaction [32]. These findings emphasize the importance of early orthodontic screening and preventive strategies to reduce the need for complex interventions [33].

The overall association between referral source and malocclusion type suggests that treatment-seeking pathways may vary according to clinical presentation. Previous studies have shown that perceived aesthetic concerns and clinical severity may influence individuals’ motivation to seek orthodontic treatment [34, 35]. In addition, certain malocclusion-related features, such as increased overjet, may require early professional recognition because of their association with dental trauma risk [36]. However, because several referral and malocclusion subgroups contained sparse data, the present findings do not allow reliable conclusions regarding specific referral categories or individual malocclusion classes. Therefore, category-specific patterns should be interpreted cautiously.

The relationship among information sources, referral patterns, and age highlights the growing role of internet-based information sources in shaping health awareness. The predominance of the internet as a primary information source among self-referred individuals reflects broader trends in online health information-seeking behavior [37]. However, because specific social media platforms were not evaluated separately, the present findings should be interpreted as reflecting internet-based information acquisition rather than the influence of social media specifically. In contrast, adolescents rely more heavily on dentists and family members, indicating that treatment decisions in younger populations are influenced by external guidance [38, 39].

This study has several limitations. The sample consisted of treatment-seeking individuals from a single university clinic, which may limit generalizability of the findings. The cross-sectional design also precludes causal inference. Future multicenter studies with larger and more diverse populations, as well as longitudinal designs, are warranted. Although participants were equally distributed across the three age groups, the age categories were defined according to developmental stages rather than equal chronological intervals. While this approach was considered more appropriate for evaluating orthodontic awareness and treatment-seeking behavior across clinically relevant age periods, the unequal age-range widths may have introduced heterogeneity within the groups. This classification is consistent with previous studies evaluating orthodontic treatment awareness and self-perceived treatment need in adolescent and young adult populations, as well as evidence indicating that health awareness and treatment-related decision-making vary across developmental stages [8, 9, 38]. Furthermore, the malocclusion groups were not equally represented, with relatively small numbers of participants in the Class I and Class II subdivision groups. As the sample consisted of consecutive treatment-seeking individuals, the distribution reflected the characteristics of the available clinical population; however, it may have influenced the statistical power of malocclusion-specific comparisons. In addition, some contingency tables contained low expected cell frequencies due to the unequal distribution of malocclusion groups. To verify the robustness of the overall associations, the Monte Carlo exact test (Fisher–Freeman–Halton extension) was additionally performed, which confirmed the chi-square findings for the overall association in all affected tables (p=0.0016, p<0.001, and p=0.0016 for the chief complaint–malocclusion type, chief complaint–age group, and referral source–malocclusion type associations, respectively). However, the specific subgroup differences identified through adjusted residuals should be interpreted with caution given the sparse cell counts in several categories. Another limitation is that malocclusion classification was based on clinical and dental findings rather than cephalometric skeletal assessment. Consequently, the skeletal severity and underlying skeletal relationships of the malocclusions could not be evaluated separately. Future studies incorporating comprehensive cephalometric analysis may provide a more detailed understanding of the relationship between malocclusion characteristics, aesthetic perception, and treatment awareness. Another limitation is that the IOTN-AC assessment was based on participants’ self-perception rather than clinician evaluation. Although this approach was consistent with the aim of assessing awareness and aesthetic perception, subjective interpretation of the photographic scale may have influenced the reported scores. Despite these limitations, the present study provides clinically relevant insights into the complex interplay between malocclusion awareness, aesthetic perception, and orthodontic treatment-seeking behavior in a treatment-seeking population. Unlike previous studies that primarily focused on orthodontic treatment need or aesthetic perception alone, the present study evaluated these factors together with referral patterns and sources of orthodontic knowledge. This comprehensive approach offers a broader understanding of the multifactorial determinants of orthodontic treatment demand and may contribute to more patient-centered orthodontic assessment and treatment planning.

Conclusion

Orthodontic treatment demand is influenced not only by the clinical severity of malocclusion but also by individuals’ aesthetic perceptions and awareness. Higher IOTN-AC scores were associated with lower smile satisfaction, indicating that perceived aesthetic impairment is a key driver of treatment-seeking behavior. The overall association between referral source and malocclusion type further suggests that treatment-seeking pathways may vary according to clinical presentation, although category-specific differences should be interpreted with caution given the sparse subgroup data. These findings emphasize the importance of integrating patients’ perceptions into orthodontic evaluation to improve patient-centered outcomes.

Abbreviations

CLP

Cleft Lip and Palate

IOTN

Index of Orthodontic Treatment Need

IOTN-AC

Aesthetic Component of the Index of Orthodontic Treatment Need

SD

Standard Deviation

Authors’ contributions

SMC: Conceptualization, Study design, Methodology, Data curation, Formal analysis, Literature review, Supervision, Writing – original draft preparation, Critical revision of the manuscript, Writing – review and editing. KAC: Data collection, Investigation, Literature review, Visualization, Writing – review and editing. MT: Methodology, Statistical analysis, Validation, Writing – review and editing.

Funding

Not applicable.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Mersin University Clinical Research Ethics Committee (Approval No: 2024/637). The study protocol was conducted in accordance with the principles of the Declaration of Helsinki, and prior to the procedures, written informed consent was obtained from all participants and from the parents and/or legal guardians of minors.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.