The board of trustees of the American Association of Orthodontists requested that a panel of orthodontic experts in dental sleep medicine update the 2019 American Association of Orthodontists white paper and create a document to guide practicing orthodontists on the role of orthodontics in managing obstructive sleep apnea. The present updated white paper summarizes the task force's findings and recommendations.

OBJECTIVES:To evaluate the dentoalveolar effects on the upper first premolars after two maxillary expansion protocols-rapid maxillary expansion (RME) and alternate rapid maxillary expansion and constriction (Alt-RAMEC)-before and 6 months after treatment. MATERIALS AND METHODS:Cone-Beam Computed Tomography (CBCT) images of 20 patients who underwent maxillary expansion with the Hyrax appliance under two activation protocols were evaluated. Dental and alveolar measurements at the premolar region (tooth length, alveolar bone level and thickness) were measured before (T1) and after expansion (T2). Intra- and inter-examiner reliability was determined using Intraclass Correlation Coefficients. Statistical analyses included the Shapiro-Wilk test and Student's t-tests for paired and independent samples. Statistical significance was set at 5%. RESULTS:A significant increase in interpremolar distances was observed, both in the radicular and coronal levels (4.29 mm and 4.97 mm), with greater coronal than root distance. This was more evident in the Alt-RAMEC group. Alveolar thickness showed an average reduction of 0.54 mm after expansion. CONCLUSIONS:The expansion effects of the two protocols were similar in the premolar region, resulting in a reduction in alveolar thickness. A tendency for greater tooth inclination and root reduction was observed after the Alt-RAMEC protocol. TRIAL REGISTRATION:IRB: 42856915.1.0000.5336.

Artificial intelligence (AI) and remote monitoring technologies are increasingly used in orthodontics to improve treatment efficiency and reduce in-office visits. Dental Monitoring (DM) introduced Smart STL, which generates 3D digital models from smartphone scans; however, its accuracy following significant dental changes such as rapid maxillary expansion has not been fully validated. This retrospective pilot study evaluated the clinical accuracy of Smart STL models following Hyrax-assisted maxillary expansion compared with intraoral scanner models. Records of 11 patients who underwent Hyrax-assisted expansion and were monitored using Dental Monitoring were analyzed. For each patient, a post-expansion intraoral scan obtained with a 3Shape scanner was compared with a Smart STL model generated through the DM platform. Superimpositions were performed using MEDIT software with automatic bestfit alignment, and two outcome measures were assessed: root mean square (RMS) deviation and absolute mean deviation. Clinical agreement between models was evaluated using the American Board of Orthodontics (ABO) clinical threshold of <0.5 mm. Analysis demonstrated statistically significant differences between Dental Monitoring Smart STL models and intraoral scanner models for both RMS and absolute mean deviation; however, all discrepancies remained below the ABO clinical threshold of 0.5 mm. These findings indicate that although statistically significant differences were present, they were clinically insignificant, supporting the clinical accuracy of Smart STL models for remote orthodontic monitoring following rapid maxillary expansion.

To investigate quality of life changes, family impacts, and predictors of successful overjet (OJ) reduction in patients treated with removable functional appliances for prominent upper front teeth. A total of 86 cases were analyzed, whose ages ranged from 11 to 13 years (median 12; 43

With the increasing integration of digital technologies in orthodontics, bonding techniques have evolved from traditional methods to more sophisticated virtual and artificial Intelligence -assisted workflows. The purpose of this study was to determine if there are any clinically significant differences between direct, virtual indirect, and artificial intelligence bonding techniques. Methods This in vivo study analyzed 840 teeth selected from 14 patients undergoing orthodontic treatment with full fixed appliances. Anatomical superimpositions were performed, data was collected as both numerical values and color-coded deviation maps to assess the differences between direct, Artificial intelligence (AI) and virtual indirect bonding techniques. Results The intraclass correlation coefficient test showed good correlation (0.894). The Kruskal-Wallis comparison showed a statistically significant difference when comparing direct to virtual indirect and direct to AI. Descriptive statistics showed 4 values with clinically significant differences (>0.50mm) when comparing direct to virtual indirect. Descriptive statistics showed 3 values with clinically significant differences when comparing direct to AI bonding technique. Conclusion We found statistically and clinically significant differences between AI and virtual indirect when compared to direct bonding. With our data, we could infer that if we compare AI Vs virtual indirect, there might not be any clinically significant differences since the differences between them fall below 0.25mm.

INTRODUCTION:Nasal Airway Obstruction is a common problem affecting 30% of the general population and significantly impacts the quality of life. The objective of this research was to determine nasal airway obstruction prevalence in a population seeking orthodontic treatment using the Nasal Obstruction Symptom Evaluation (NOSE) scale and to determine if practice modalities play a role. METHODS:The sample consisted of 431 patients seeking orthodontic care who completed the NOSE survey at an orthodontic residency program, a corporate practice, and a private practice. RESULTS:Obstruction was not correlated with age, gender, or trauma (p < 0.05). The mean of all scores was 8.4 ± 14.7 (95% confidence interval [CI]: 7.0-9.8), ranging from 0 to 80 on a scale of 100. The prevalence of nasal airway obstruction in the orthodontic population was: mild or above-43.9% (95% CI: 39.1%-48.7%); moderate or above-11.4% (95% CI: 8.6%-14.9%); severe-2.6% (95% CI: 1.3%-4.5%); and extreme-0.2% (95% CI: 0.0%-1.3%). The Kruskal-Wallis test showed no significant difference among patients at the three locations. CONCLUSIONS:The Prevalence of Nasal Airway Obstruction (Mild to Extreme) in orthodontic populations was 43.9% (95% CI: 39.1%-48.7%), representing or slightly exceeding that of the general population. There was no significant difference between an orthodontic residency program, a corporate practice, and a private practice, indicating that the results are widely applicable. Orthodontists are encouraged to use the NOSE scale as a risk assessment tool, providing additional healthcare services to patients and potentially improving their quality of life.

OBJECTIVES:The purpose of this study was to determine if there are any clinically significant differences between direct, virtual indirect, and artificial intelligence (AI) bonding techniques. MATERIALS AND METHODS:This in vivo study analysed 840 teeth selected from 14 patients undergoing orthodontic treatment with full fixed appliances. Anatomical superimpositions were performed, and data were collected as both numerical values and colour-coded deviation maps to assess the differences between direct, AI, and virtual indirect bonding techniques. RESULTS:The intraclass correlation coefficient test showed good correlation (0.894). The Kruskal-Wallis comparison showed a statistically significant difference when comparing direct to virtual indirect and direct to AI. Descriptive statistics showed 4 values with clinically significant differences when comparing direct to virtual indirect. Descriptive statistics showed 3 values with clinically significant differences when comparing direct to AI. Root mean square (RMS) discrepancies exceeding 0.50 mm were found in four tooth types (AI vs. Direct) and three (Clinician vs. Direct). CONCLUSIONS:We found statistically and clinically significant differences between AI and virtual indirect when compared to direct bonding. With our data, we could infer that if we compare AI versus virtual indirect, there might not be any clinically significant differences since the differences between them fall below 0.25 mm.

OBJECTIVE:To explore the relationship between early dentofacial orthopaedic treatment, improvement in the width of oropharynx and nasopharynx, and quality of life. MATERIALS AND METHODS:Thirty-three prepubertal children with skeletal Class III (median age 9 years; 56% females) received treatment with a maxillary expander and facemask. These subjects were matched with two control groups: one comprising an equal number of untreated Class III individuals, and the other consisting of untreated Class I controls. Cephalograms were analysed, and both children and their parents self-administered the Child Perceptions Questionnaire, Parental-Caregiver Perceptions Questionnaire and Family Impact Scale. RESULTS:Treated Class III cases showed significant increases in the nasopharyngeal and oropharyngeal airway width (p ≤ 0.033), with greater changes in the nasopharyngeal width compared to untreated Class III cases (p = 0.040). Compared to untreated Class III and Class I groups, treated Class III cases exhibited reduced mandibular prominence and sagittal skeletal Class, increased overjet, overbite, vertical facial dimension, and greater retroclination and retrusion of mandibular incisors (p ≤ 0.011). Prior to and following orthodontic treatment, Class III cases reported a lower quality of life across all dimensions compared to Class I controls (p ≤ 0.032). An increase in maxillary anterior movement and oropharyngeal width correlated with a decrease in functional limitations reported by children (r = -0.411-(-0.413)); (p ≤ 0.022). CONCLUSION:Maxillary expansion and protraction in prepubertal Class III children can enhance upper airways width, and children associate these improvements with a reduction in functional limitations.

INTRODUCTION:This study aimed to investigate the accuracy of dental model printing using 2 different layer height settings and how these settings affect the fabrication of thermoformed retainers. METHODS:Subjects were recruited from the Department of Orthodontics at Case Western Reserve University and scanned according to specific selection criteria. A total of 30 stereolithography files were produced and used as reference files. The stereolithography files were printed at the recommended layer height of 100 μm and 170 μm with a Sprint Ray Pro 95 3-dimensional (3D) printer (Sprint Ray, Los Angeles, Calif). All printed models were scanned using the same iTero intraoral scanner (Align Technology, San Jose, Calif) as was used for the initial intraoral scan as well. The accuracy of the printed models was based on the evaluation of root mean square values resulting from 3D superimpositions. Afterward, vacuum-formed retainers were fabricated. The vacuum-formed retainers were evaluated by the patient and an American Board of Orthodontics-certified orthodontist. RESULTS:No difference was observed in the maxillary arch (P = 0.85) and the mandibular arch accuracy (P = 0.08) by assessing the root mean square values. No difference was observed in the doctor retainer score of the maxillary retainers (P = 0.37) and the mandibular retainers (P = 0.77). There was no difference in the patient retainer score of the maxillary (P = 0.08) and the mandibular retainers (P = 0.22) when comparing retainers. Conversely, less printing time was observed when printing the models with 170 μm compared with 100 μm (P <0.001). CONCLUSIONS:The accuracy of a dental model printed with a Sprint Ray Pro 95 3D printer was not affected by the 100 or 170 μm layer height. Orthodontists and patients did not detect a statistically significant difference in retainer fit.

The aim of this study was to evaluate the correlation of the volume and minimum axial area (MAA) measurements between different upper and lower boundaries used for oropharyngeal airway assessment. Cone Beam Computed Tomography (CBCT) scans of 49 subjects taken for pre-orthognathic surgical planning were obtained retrospectively from the archives (n = 49; 32 females, 17 males; mean age = 20.9 ± 5.22). Volume and MAA of the oropharyngeal airway were measured in 32 different airway segmentations created with four different upper and eight different lower boundaries using the Dolphin3D (Dolphin Imaging Management Solutions, Chatsworth, California, ABD) software. All measurements were performed by the same examiner and were repeated 2 weeks apart. The correlation between the measurements was evaluated with the Pearson correlation test. Intra-observer reliability was calculated with the intra-class correlation coefficient. Volume and MAA showed excellent intra-observer reliability (0.997 and 0.999 intraclass correlation coefficients, respectively) and a high level of positive correlation (r = 0.896–0.999, and r = 0.859-1.00, respectively) for all the measurements. All measurements between different lower and upper boundaries showed a high correlation. It was found that the lower and upper limits assessed in this study can be used safely in future upper airway studies according to the study design.

Objective:The skeletal pattern of a Class II malocclusion can increase the probability of problems with oronasal function. This study aimed to analyze the relationship between the clinical effects of dentofacial orthopedics and oral function in deficient mandibles with proclined maxillary incisors during puberty. Methods:Of 120 randomized participants, 63 with complete records were included in the final analysis (median age: 12 years; 38% females). Subjects were treated with the Sander bite jumping appliance (BJA; n = 34) or the twin block appliance (TB; n = 29) with a maxillary expansion jackscrew. The growth effect was monitored by comparisons with the findings in 63 historical untreated Class II cases. Palatal scans and cephalograms were analyzed before and at one-year intervals after treatment. Both the children and their parents reported oral symptoms and functional limitations (FLs). Results:Palatal volume increased in both appliance groups (BJA: 860.3 mm3 [95% confidence interval (CI): 481.6-1,150.1] and TB: 958.9 mm3 [95% CI: 555.1-1,446.3]) in comparison with the untreated group (287.0 mm3 [95% CI: 193.4-426.5]; P < 0.001). However, the treatment did not affect airways. In comparison with the untreated group, the treated groups showed greater mandibular length and reduced overjet (P ≤ 0.002). No linear correlations were found between changes in dentofacial, palatal, or airway characteristics and changes in oral function. However, the palatal volume of the treated children who experienced improvement in FLs increased more than that of those who showed no FL improvement (1,220.6 mm3 [95% CI: 772.6-1,616.4] and 760.5 mm3 [95% CI: 491.6-1,002.1]; P = 0.035). Conclusions:Both appliances promoted mandibular growth and increased oral space, but did not significantly influence the measured upper airway widths.

OBJECTIVES:To validate the third palatal rugae as stable reference landmarks for the superimposition of maxillary digital models in premolar extraction cases. MATERIALS AND METHODS:Maxillary intraoral scanning digital models of 22 extraction patients, obtained before (T0) and after approximately 4 months of incisor retraction (T1), were superimposed using the automated best-fit of the third palatal rugae and palatine raphe. Voxel-based registration of CBCT scans served as the gold standard. Point-to-point displacements between T0 and T1 of the central incisors and cuspids were calculated for both methods. RESULTS:The mean differences (mismatch) between methods in the combined vertical and horizontal perspectives of the central incisors were 0.08 mm (right) and 0.12 mm (left), respectively. For the cuspids, the mean 2D differences were 0.02 mm (right) and 0.07 mm (left). The pure horizontal displacement differences of the maxillary central incisors were 0.13 mm (right) and 0.04 mm (left). For the cuspids, the differences were: 0.05 mm (right) and 0.03 mm (left). No statistically significant differences were found between the intraoral-scan and CBCT measurements for either the central incisors or cuspids after maxillary superimpositions. The differences between the two methods were smaller than 0.2 mm in all comparisons. CONCLUSIONS:Digital superimposition of the third palatal rugae of models obtained with intraoral scans is a valid method for the 3D evaluation of the short-term (4-month) dental changes in the anterior region of the maxilla in cases of premolar extraction followed by incisor retraction. TRIAL REGISTRATION:ClinicalTrials.gov: NCT03089996 and NCT05281588.

Introduction Since its introduction in 1931, cephalometric analysis has played a critical role in orthodontic diagnosis by allowing clinicians to differentiate between skeletal and dentoalveolar patterns of malocclusion, which is essential for appropriate treatment planning. Conventional cephalometric analysis, based on manual landmark tracing, is time-consuming and remains labor-intensive, with digital imaging software still requiring up to 15 minutes for landmark identification. To address this limitation, artificial intelligence (AI) is being integrated into orthodontic software to automate landmark detection. However, the accuracy and reliability of these AI-based systems remain uncertain, and further research is necessary to confirm their accuracy and reliability without human oversight. Aim To evaluate techniques for automatic digitization of cephalograms using artificial intelligence (AI) algorithms, highlighting their strengths and weaknesses and reviewing the percentage of success in localizing each cephalometric point. Methods Thirty lateral cephalograms from the Mathew’s Collection of the AAOF Legacy Collection were digitized and traced by three calibrated orthodontists. The same radiographs were uploaded to AI-based machine learning programs Orthodx, Angel, and WebCeph. Dolphin software was used to extract x- and y-coordinates for 12 cephalometric landmarks, comprising 8 skeletal and 4 dental points. Mean radial errors (MRE) were assessed relative to thresholds of 1.0 mm, 1.5 mm, and 2 mm to compare successful detection rates (SDR). One-way ANOVA at a significance level of P < .05 was used to compare MRE and SDR. SPSS (IBM v27.0) was used for data analysis. Results Experimental results showed that both human tracers and AI-based methods achieved accuracy above 87% using the 2 mm precision threshold, which is considered acceptable in clinical practice. There were no significant differences in MRE or SDR among the evaluated methods based on ANOVA analysis. A marked difference in time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark. Conclusions Errors in both human tracing and AI-assisted methods showed clinically acceptable accuracy above 87% at the 2 mm threshold. AI assistance may be used in orthodontic practice without compromising accuracy while increasing efficiency in routine clinical and research settings.

ABSTRACT Objective To explore which effects of jaw orthopaedics in class II during puberty are related to daily functioning and family impacts. Materials and Methods Seventy‐one subjects in their pre‐pubertal and peri‐pubertal stages with skeletal class II and procumbent incisors (median age 12 years; 56% females) were treated with the Sander bite jumping appliance (BJA; N = 37) and Twin block (TB; N = 35). They were matched by the same number of untreated class II subjects. Lateral cephalograms before and after treatment (one‐year distance) were analysed. Children's and parental perspectives of oral health‐related quality of life (OHRQoL) were assessed by the Child Perceptions Questionnaire, Parental‐Caregiver Perceptions Questionnaire and Family Impact Scale Results The change in pharyngeal airway width in treated groups was not significantly greater than in the group without orthodontic treatment. Treated groups, compared to the untreated group, demonstrated an increase in mandibular length and a reduction in sagittal skeletal intermaxillary discrepancy ( p ≤ 0.002). Retroclination of the maxillary incisors, accompanied by a reduction in overjet, overbite and upper lip prominence, was observed in both treated groups compared to the untreated ( p ≤ 0.001). Parents of children in the TB group reported a reduction in impairment of OHRQoL when compared to BJA ( p = 0.003). There were no correlations between the change in the airway width, dentofacial characteristics and OHRQoL. Conclusion Treatment with both appliances stimulated mandibular growth, retroclined maxillary incisors and improved facial profile and OHRQoL. However, the changes in the airway were not greater than normal growth and were not related to alterations in other dentofacial characteristics or OHRQoL. Trial Registration ClinicalTrials.gov identifier: NCT03455634

Background: Orthognathic surgery significantly alters the dimensions of the pharyngeal airway. This study’s objective was to assess alterations in the pharyngeal airway volume via cone-beam computed tomography (CBCT) after orthognathic surgery in patients with skeletal Class III malocclusion. Methods: This retrospective study analyzed CBCT images from 23 patients with skeletal Class III malocclusion (13 females, 10 males), who were categorized into two groups based on the surgical approach: double-jaw and single-jaw surgery. The double-jaw group included 13 patients who underwent bilateral sagittal split osteotomy (BSSO) and Le Fort I osteotomy, whereas the single-jaw group included of 10 patients who had underwent BSSO only. CBCT images were evaluated both before surgery and at a minimum of three months after surgery. The oropharyngeal volume (OP), nasopharyngeal volume (NP), total airway volume, posterior airway space (PAS), and the most constricted area at the base of the tongue (minAx) were measured. Statistical analyses were performed using either paired t-tests or Wilcoxon signed-rank tests depending on data normality, with a significance level set at p < 0.01. Results: In the double-jaw group, a significant volumetric increase was observed in the nasopharynx (5316 ± 1948 mm3 to 6064 ± 1899 mm3; p = 0.010) and oropharyngeal volume decreased from 17,097 ± 5675 mm3 to 14,290 ± 5835 mm3; however, this reduction was not statistically significant (p = 0.017). In contrast, the single-jaw group showed a significant reduction in oropharyngeal volume from 15,620 ± 5040 mm3 to 12,444 ± 4701 mm3 (p = 0.010), with no significant change in nasopharyngeal volume (p = 0.551). Total airway volume significantly decreased only in the single-jaw group (from 20,452 ± 7754 mm3 to 16,846 ± 6529 mm3, p = 0.010). Additionally, both groups exhibited marked decreases in PAS and minimum axial area values (all p < 0.01). Conclusions: Orthognathic surgery led to a significant volumetric increase in the nasopharynx in the double-jaw group, whereas the oropharynx volume significantly decreased only in the single-jaw group. Additionally, both surgical approaches resulted in a marked reduction in PAS and minimum axial area values, highlighting a notable impact on posterior airway dimensions.

INTRODUCTION:Machine learning, a common artificial intelligence technology in medical image analysis, enables computers to learn statistical patterns from pairs of data and annotated labels. Supervised learning in machine learning allows the computer to predict how a specific anatomic structure should be segmented in new patients. This study aimed to develop and validate a deep learning algorithm that automatically creates 3-dimensional surface models of human teeth from a cone-beam computed tomography scan. METHODS:A multiresolution dataset, including 216 × 272 × 272, 512 × 512 × 512, and 576 × 768 × 768. Ground truth labels for teeth segmentation were generated. Random partitioning was applied to allocate 140 patients to the training set, 40 to the validation set, and 30 scans for testing and model performance evaluation. Different evaluation metrics were used for assessment. RESULTS:Our teeth identification model has achieved an accuracy of 87.92% ± 4.43% on the test set. The general (binary) teeth segmentation model achieved a notably higher accuracy, segmenting the teeth with 93.16% ± 1.18%. CONCLUSIONS:The success of our model not only validates the efficacy of using artificial intelligence for dental imaging analysis but also sets a promising foundation for future advancements in automated and precise dental segmentation techniques.

OBJECTIVES:To evaluate the impact of printer technology and print orientation on the accuracy of directly printed retainers. MATERIALS AND METHODS:Digital retainers were printed with two different printing technologies: digital light processing (DLP) and stereolithography (SLA), using two different orientations: 0° and 90°. After printing, the retainers (n = 40) were scanned using cone-beam computed tomography. The DICOM files were then converted into standard tessellation language (STL) files. Comparison of the printed retainers with a master file was done by superimposition using a three-dimensional (3D) best-fit tool in Geomagic software. A ±0.25 mm tolerance was set to detect differences between the superimposed files. Statistical analysis was conducted (Kruskal-Wallis and Wilcoxon-Mann-Whitney tests, with Bonferroni correction). RESULTS:The lowest median average deviation was observed for the DLP horizontally printed models (median, [interquartile range (IQR)] = 0.01 mm, [-0.01, 0.02]) followed by the SLA horizontally printed retainers (median, [IQR] = 0.05 mm, [0.03, 0.07]). The highest median inside the tolerance levels ratio was observed for the horizontally SLA printed retainers (median, [IQR] = 78.9%, [74.4, 82.4%]) followed by the horizontally DLP printed retainers (median, [IQR] = 78.2%, [74.5, 80.7%]). CONCLUSIONS:Both technologies (DLP and SLA) showed 3D printed results compatible with orthodontic clinical needs. Printing orientation was more important than printer type regarding its accuracy. Additional studies are needed to evaluate the accuracy of direct printed appliances clinically.

BACKGROUND AND OBJECTIVE:The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. MATERIALS AND METHODS:An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. RESULTS:Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. CONCLUSION:Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.

Objective: The aim of this study was to assess the risk of sleep-disordered breathing (SDB) in orthodontic patients and to evaluate the influence of sex, age, and orthodontic treatment in a cohort of subjects using the Pediatric Sleep Questionnaire (PSQ) screening tool. Methods: Parents of 245 patients aged 5-18 years (11.4 +/- 3.3 years) were invited to participate in the study by answering the PSQ, which has 22 questions about snoring, sleepiness, and behavior. The frequency of high and low risk was calculated for the full sample. Multiple logistic regression was used to assess the association among sex, age, orthodontic treatment, rapid maxillary expansion (RME), and body mass index (BMI) with SDB. A significance level of 5% (P < .05) was adopted in all tests. Results: A high risk of SDB was found in 34.3% of the sample. No sex and BMI difference was found for the risk of SDB. The high risk of SDB was significantly associated with younger ages (OR = 1.889, P = .047), pre-orthodontic treatment phase (OR = 3.754, P = .02), and RME (OR = 4.157, P = .001). Limitations: Lack of ear, nose and throat-related medical history. Conclusion: Children showed a 1.8 higher probability of having a high risk of SDB compared with adolescents. Patients before orthodontic treatment and patients submitted to RME showed a high risk of SDB.