CBCT-based three-dimensional phenotyping of skeletal Class II malocclusion in Yemeni adults: principal components and cluster analysis

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Bin Hafedh, Ramy Ishaq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8367021/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To reduce 63 CBCT variables into principal components (PCs) describing major dimensions of skeletal Class II variation in Yemeni adults, derive phenotypic subgroups by clustering, and outline treatment implications. Materials and methods Pretreatment CBCT scans of 120 adults (16–30 years) with skeletal Class II were analyzed. Variables were z‑standardized; PCA with varimax rotation retained components with eigenvalues > 1. K‑means clustering on PC scores yielded phenotypes. Between‑cluster differences used ANOVA/Kruskal–Wallis (α = 0.05). Reliability was assessed using intraclass correlation coefficients (ICC). Results Seven PCs explained 60.2% of total variance. Five clusters with distinct skeletal and dentoalveolar patterns were identified; key variables differed significantly among clusters. ICCs exceeded 0.85 across variables, indicating excellent measurement reliability. Conclusions CBCT‑based PCA and clustering uncovered five clinically coherent Class II phenotypes that map to different management pathways (e.g., growth modification or camouflage for hypodivergent patterns; vertical control/TADs and, in severe cases, combined orthodontic–orthognathic care for hyperdivergent open‑bite–prone patterns). Clinical relevance Multivariate CBCT phenotyping clarifies heterogeneity in Class II malocclusion and can guide case‑specific mechanics and surgery decisions in adult patients. Class II malocclusion cone‑beam computed tomography principal component analysis cluster analysis craniofacial phenotype orthodontic treatment planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Skeletal Class II malocclusion is among the most common orthodontic problems and is characterised by considerable heterogeneity in sagittal, vertical and dentoalveolar relationships. This heterogeneity contributes to variability in treatment needs and outcomes, particularly in borderline cases where clinicians must choose between growth modification, orthodontic camouflage and orthognathic surgery. Conventional two-dimensional (2D) cephalometry remains widely used but is limited by projection errors, superimposition and an inability to represent complex three-dimensional (3D) relationships. Cone-beam computed tomography (CBCT) provides isotropic 3D data and allows more accurate assessment of skeletal, dental, airway and temporomandibular joint structures when justified by clinical indications. CBCT has been used to evaluate condylar morphology, tongue and hyoid position, periodontal support and cranial base morphology in various malocclusions, including Class II. However, there is a paucity of population-specific 3D phenotypic data for Middle Eastern and Yemeni populations, despite evidence that malocclusion profiles, early tooth loss and deleterious oral habits differ from those in Western cohorts. Beyond single-variable analysis, multivariate approaches such as principal component analysis (PCA) and cluster analysis (CA) allow dimensionality reduction and identification of clinically coherent sub-phenotypes. Recent CBCT-based work in skeletal Class III malocclusion and facial asymmetry has shown that PCA/CA can reveal distinct craniofacial patterns with implications for diagnosis and surgical planning. This study builds on previous thesis work that systematically characterised Class II phenotypes in Yemeni adults using CBCT-derived measurements, PCA and CA, and reframes those findings to emphasise their clinical implications. [ 1 – 8 , 9 – 11 , 19 , 23 , 24 , 29 , 31 , 33 – 35 ] Objectives This study aimed to: (1) use PCA to reduce 63 CBCT variables into a smaller number of principal components describing major dimensions of variation in skeletal Class II malocclusion in Yemeni adults; (2) apply K-means cluster analysis to derive homogeneous phenotypic subgroups; and (3) discuss the clinical implications of these CBCT-derived phenotypes for individualised treatment strategies, particularly with respect to growth modification, orthodontic camouflage and open-bite management with temporary anchorage devices or orthognathic surgery. Methods Study design and ethical approval This cross-sectional observational study was conducted at the Faculty of Dentistry, Sana’a University, and collaborating private orthodontic practices in Sana’a, Yemen. CBCT scans were obtained from routine pre-treatment records of patients seeking orthodontic care. The study protocol was approved by the Medical Ethics Committee, Faculty of Dentistry, Sana’a University (Approval code: OR:19/11/2023), in accordance with the Declaration of Helsinki. Written informed consent for diagnostic imaging and secondary use of anonymised data was obtained from all participants at the time of CBCT acquisition. Participants The sample comprised 120 Yemeni adults with skeletal Class II malocclusion (64 females, 53.3%; 56 males, 46.7%) aged 16–30 years. Inclusion criteria were: Yemeni nationality; age 16–30 years; skeletal Class II malocclusion defined as ANB ≥ 4° and Wits appraisal greater than 2 mm; at least unilateral Angle Class II molar or canine relationship with a convex soft-tissue profile; full permanent dentition excluding third molars; and availability of high-quality pre-treatment CBCT records. Exclusion criteria were: craniofacial syndromes or cleft lip/palate; severe facial asymmetry; history of significant facial trauma, temporomandibular joint disorders or systemic diseases affecting bone metabolism; missing teeth other than third molars or impacted/blocked-out teeth; and poor-quality or incomplete CBCT scans. These criteria were chosen to minimise heterogeneity and to ensure that the observed variation primarily reflected phenotypic differences in skeletal Class II malocclusion. Sample characteristics are summarised in Table 1 . Table 1 Variable Value Sample size, n 120 Age range, years 16–30 Female, n (%) 64 (53.3) Male, n (%) 56 (46.7) Skeletal Class II (ANB ≥ 4°, Wits > 2 mm) 120 (100) At least unilateral Angle Class II molar/canine relation 120 (100) Convex soft-tissue profile 120 (100) CBCT acquisition All CBCT scans were acquired using a PaX-Flex 3D P2 unit (Vatech, Korea) under a standardised protocol: field of view 15 × 15 cm, isotropic voxel size 0.3 mm, 85–90 kV, 10 mA and exposure time approximately 17 s. Patients were positioned in natural head posture with the Frankfort horizontal plane parallel to the floor and the midsagittal plane perpendicular to the floor. Teeth were in maximum intercuspation and lips were relaxed. A lead apron was used for radiation protection, and all scans were taken by the same experienced radiology technician to ensure reproducibility. DICOM datasets were exported and imported into Invivo 6.0 (Anatomage, San Jose, CA, USA) for 3D reconstruction, landmark identification and measurement. [ 19 , 23 , 24 , 29 ] Landmark identification and measurements Thirty craniofacial landmarks (skeletal, dental and selected soft-tissue points) were identified in three planes of space. Landmarks were chosen to capture cranial base length and angulation; maxillary and mandibular position and lengths; vertical dimensions (anterior and posterior facial height, mandibular plane angles); maxillo-mandibular relationships (overjet, overbite, Wits-equivalent measures); incisor position and inclination; and selected airway/soft-tissue parameters where visible. Bilateral landmarks were traced on both sides and averaged to reduce the influence of minor asymmetry. From these landmarks, 63 three-dimensional variables were computed, including linear, angular and ratio measures, providing comprehensive coverage of cranial base, maxillary, mandibular, intermaxillary and dental/soft-tissue dimensions. [ 19 , 23 , 24 , 31 ] Reliability assessment To assess intra- and inter-examiner reliability, 20 CBCT scans were randomly selected. All landmarks were traced by the primary examiner and re-traced after a two-week interval, and independently traced once by a second examiner. Intraclass correlation coefficients exceeded 0.85 for all variables, indicating excellent reliability; no systematic differences were detected on paired t-tests or Wilcoxon signed-rank tests (p > 0.05). Principal component analysis [ 18 , 23 , 26 , 30 , 32 ] Continuous variables were checked for normality using the Shapiro–Wilk test and inspection of histograms and Q–Q plots. Data were standardised (z-scores) and analysed with IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). Principal component analysis (PCA) with varimax rotation and Kaiser normalisation was applied to the 63 variables. Components with eigenvalues greater than 1 were retained. The number of components was determined by eigenvalues and scree plot inspection, supported by clinical interpretability. Variables with loadings ≥ |0.50| were considered meaningful contributors to each component, and component scores were saved for subsequent cluster analysis. Cluster analysis and statistical testing [ 18 , 23 , 26 , 30 , 32 ] K-means cluster analysis with Euclidean distance was performed on the retained component scores. Solutions with k ranging from 3 to 7 were examined. The final five-cluster solution was selected based on stability across random initialisations, proportion of variance explained and clinical interpretability of the resulting phenotypes. Canonical discriminant analysis was used to visualise inter-cluster separation and to inspect for outliers using prediction ellipses. One-way ANOVA (or Kruskal–Wallis tests where appropriate) was used to compare key skeletal and dental variables among clusters, with post-hoc pairwise comparisons adjusted for multiple testing. Statistical significance was set at p < 0.05. Results Principal components Seven principal components with eigenvalues greater than 1 were retained, together explaining 60.2% of the total variance (Table 2 ). PC1 had an eigenvalue of 4.275 and accounted for 10.2% of the variance; PC2, PC3 and PC4 explained 9.3%, 9.1% and 8.8%, respectively, and PCs 5–7 each contributed between 5.8% and 8.7%. PC1 primarily reflected vertical skeletal pattern and cranial base length, with high positive loadings on mandibular plane angle, lower anterior facial height and cranial base length. PC2 predominantly represented maxillary incisor inclination and dentoalveolar compensation; PC3 differentiated shorter versus longer mandibles and associated posterior facial height; and PC4 related mainly to maxillary anteroposterior position and unit length. PCs 5–7 represented smaller proportions of variance and corresponded to mandibular incisor position, facial taper and the severity of sagittal discrepancy. The scree plot confirmed a clear elbow at seven components, and a 95% prediction ellipse of component scores showed no statistical outliers (Figs. 1 and 2 ). Table 2 Component Eigenvalue Proportion of variance Cumulative variance (%) PC1 4.275 10.2% 10.2 PC2 3.918 9.3% 19.5 PC3 3.813 9.1% 28.6 PC4 3.695 8.8% 37.4 PC5 3.660 8.7% 46.1 PC6 3.457 8.2% 54.3 PC7 2.445 5.8% 60.2 Cluster solution and sample distribution K-means clustering on the seven component scores yielded a five-cluster solution that balanced parsimony with clinical interpretability. Canonical discriminant plots showed good separation between clusters with minimal overlap (Fig. 3 ). Cluster sizes and distances between centroids are summarised in Table 3 . Cluster 1 contained 42 subjects (35.0%), cluster 2 contained 24 (20.0%), and clusters 3, 4 and 5 each contained 18 subjects (15.0%). Root mean square distances within clusters ranged from 0.243 to 0.340, and inter-centroid distances exceeded 2.40, indicating well-separated phenotypic groups. Table 3 Cluster Frequency (n) Percentage of sample (%) Root mean square (SD) Nearest cluster Distance between centroids 1 42 35.0 0.243 2 2.402 2 24 20.0 0.340 1 2.402 3 18 15.0 0.256 1 2.615 4 18 15.0 0.319 1 2.705 5 18 15.0 0.331 1 2.688 Phenotypic characterisation of clusters Based on component loadings and cluster centroids, the five clusters were characterised as follows. Cluster 1 represented subjects with a relatively long cranial base, mild retrusion of both maxilla and mandible, a normodivergent vertical pattern and increased overjet with deep-bite tendency. Cluster 2 showed a normal cranial base with predominantly mandibular retrusion, mildly decreased mandibular plane angle (hypodivergent pattern) and normal overbite or mild deep bite. Cluster 3 comprised individuals with a short cranial base and mandibular body, more pronounced mandibular retrusion, reduced posterior facial height and a deep-bite tendency with marked overjet. Cluster 4 exhibited a short cranial base, maxillary protrusion combined with mandibular retrusion, steep mandibular plane angle, reduced ramus height, increased facial taper and an anterior open-bite tendency or low overbite with greater inter-labial gap. Cluster 5 included subjects with a normal cranial base, mildly protrusive maxilla and mildly retrusive mandible, significantly reduced mandibular plane angle (flat mandibular plane), protrusive maxillary incisors, normal mandibular incisors and normal or slightly reduced overbite with increased overjet and shorter anterior facial height. Across clusters, one-way ANOVA revealed significant differences in key skeletal and dental variables, including mandibular plane angle, posterior facial height, ANB, maxillary and mandibular unit lengths, and incisor inclinations (Table 4 ; all p-values < 0.001). Additional cluster-level cephalometric means for all variables are presented in Table 6 and visualised in Fig. 4 . Table 4 Variable Cluster 1 (mean ± SD) Cluster 2 (mean ± SD) Cluster 3 (mean ± SD) Cluster 4 (mean ± SD) Cluster 5 (mean ± SD) p-value Mandibular plane angle (°) 37.60 ± 2.21 34.80 ± 3.93 39.83 ± 2.59 38.58 ± 1.71 48.30 ± 0.00 < 0.0001 Posterior facial height (mm) ANB (°) 6.52 ± 1.03 6.42 ± 0.91 6.34 ± 0.67 6.47 ± 0.65 6.40 ± 0.88 0.9284 Maxillary unit length (mm) 76.46 ± 2.94 72.50 ± 1.93 76.25 ± 3.50 75.62 ± 2.70 70.20 ± 0.00 < 0.0001 Mandibular unit length (mm) 96.94 ± 3.14 98.24 ± 3.34 96.26 ± 5.18 100.31 ± 3.26 97.28 ± 3.35 0.0013 Overjet (mm) Overbite (mm) U1–SN (°) 124.38 ± 3.79 120.92 ± 6.37 102.16 ± 4.30 123.43 ± 3.29 115.00 ± 0.00 < 0.0001 L1–MP (°) 96.58 ± 2.86 96.84 ± 2.68 96.70 ± 2.71 96.69 ± 2.91 96.09 ± 3.14 0.9847 Table 6 Variable Cluster 1 Mean Cluster 1 SD Cluster 2 Mean Cluster 2 SD Cluster 3 Mean Cluster 3 SD Cluster 4 Mean Cluster 4 SD Cluster 5 Mean Cluster 5 SD ANOVA p-value SNA 81.71 3.78 83.18 2.18 80.57 3.53 80.26 3.30 81.32 0.00 0.01164 SNB 74.16 1.07 76.10 2.96 76.40 4.75 74.38 3.49 75.25 0.00 0.05299 ANB 6.52 1.03 6.42 0.91 6.34 0.67 6.47 0.65 6.40 0.88 0.9284 AO-BO 3.36 1.15 4.08 1.87 5.07 1.10 3.46 1.25 3.00 0.00 1.03e-06 SN-GoGn 37.60 2.21 34.80 3.93 39.83 2.59 38.58 1.71 48.30 0.00 1.531e-19 FH-MP 29.08 1.26 27.26 3.14 29.27 1.02 24.45 2.22 28.51 0.00 5.227e-17 U1 - SN 124.38 3.79 120.92 6.37 102.16 4.30 123.43 3.29 115.00 0.00 3.352e-43 L1 - Mp 30.82 2.11 28.98 4.76 29.73 0.82 30.34 1.50 30.90 0.00 0.08302 Co-A 76.46 2.94 72.50 1.93 76.25 3.50 75.62 2.70 70.20 0.00 1.439e-08 Co-Gn 96.94 3.14 98.24 3.34 96.26 5.18 100.31 3.26 97.28 3.35 0.001271 N-ANS 47.78 1.23 45.85 1.45 47.30 1.32 50.61 4.90 47.00 0.00 5.953e-08 ANS-Me 62.28 3.48 66.76 0.89 66.47 2.10 65.06 2.06 69.70 0.00 2.656e-13 U1-PP 27.49 1.57 22.95 0.87 28.25 0.75 24.49 2.29 27.84 0.00 2.532e-28 L1-MP 96.58 2.86 96.84 2.68 96.70 2.71 96.69 2.91 96.09 3.14 0.9847 Clinical mapping of phenotypes The identified clusters aligned with clinically recognisable treatment categories. Clusters 1–3, which were mostly normo- or hypodivergent with deep-bite tendencies, appear suitable for growth-modification approaches in growing patients and orthodontic camouflage in adults, with emphasis on sagittal correction and bite opening. Cluster 4, a hyperdivergent phenotype with open-bite tendency, requires intensive vertical control, often with temporary anchorage devices for posterior intrusion and, in more severe cases, combined orthodontic–orthognathic surgery. Cluster 5, a hypodivergent phenotype with protrusive maxillary incisors, can be managed with careful anchorage planning and torque control to address incisor proclination and overjet while maintaining favourable vertical relationships. A qualitative clinical summary of each cluster and suggested treatment directions is presented in Table 5 . Table 5 Cluster Key phenotypic features Typical clinical concerns Predominant treatment direction 1 Retropositioned maxilla and mandible; long cranial base; deep bite Reduced facial convexity; deep overbite Growth modification or camouflage; sagittal advancement and bite opening 2 Mandibular retrusion; hypodivergent; normal overbite Chin deficiency; possible crowding Functional appliances in growing patients; camouflage or surgery in adults 3 Short cranial base and mandibular body; reduced posterior facial height; deep bite Marked overjet and deep overbite; short lower face Bite-opening mechanics; torque control; possible extractions 4 Maxillary protrusion plus mandibular retrusion; steep mandibular plane; open-bite tendency Increased lower facial height; anterior open bite risk Vertical control with TADs; orthognathic surgery in severe cases 5 Flat mandibular plane; mild maxillary protrusion; protrusive maxillary incisors Increased overjet; incisor proclination/gingival risk Anchorage-reinforced mechanics; torque control; selective extractions Discussion This CBCT-based multivariate analysis identified seven principal components and five distinct skeletal Class II phenotypes in Yemeni adults. The components captured major dimensions of vertical pattern, cranial base morphology, jaw length and position, and incisor inclination, while the clusters integrated these dimensions into clinically recognisable subgroups. Mandibular retrusion was a common feature across most clusters, but vertical pattern and dentoalveolar compensation varied substantially, supporting the concept that Class II malocclusion is a heterogeneous spectrum rather than a single entity. The presence of a hyperdivergent, open-bite-prone cluster (cluster 4) is particularly relevant clinically, as such patients often require rigorous vertical control, temporary anchorage device-supported mechanics and, in severe cases, orthognathic surgery. Hypodivergent clusters with deep bite (clusters 2 and 5) are generally more amenable to growth modification or camouflage, though careful control of incisor torque and periodontal support is crucial, especially when significant retraction is planned. The phenotypic clusters described here broadly align with patterns reported in previous phenotyping studies using PCA and cluster analysis in skeletal Class III malocclusion and facial asymmetry, while adding novel information about skeletal Class II variation in a Yemeni adult population. A major strength of this study is the use of 3D CBCT data and a comprehensive variable set, combined with robust multivariate modelling and stringent inclusion criteria. However, the clinic-based, cross-sectional design limits generalisability to the wider Yemeni population and precludes conclusions about growth or long-term stability within each phenotype. Determining the optimal number of clusters also involves clinical judgement, even when supported by statistical criteria, and alternative partitions might be defensible. In addition, treatment recommendations are inferential, based on mapping clusters to findings from external treatment studies, rather than on prospective outcome data for each cluster. Future research should validate these phenotypes in independent samples and other ethnic groups, stratify Class II into divisions 1 and 2 to explore potential differences, and prospectively assess treatment outcomes and stability by cluster. Integration of CBCT-based phenotyping with artificial intelligence tools and genetic analyses may further advance precision orthodontics. [ 9 – 11 , 18 – 20 , 23 – 26 , 30 – 32 , 33 – 35 , 40 ] Conclusions CBCT-based PCA and cluster analysis revealed five distinct skeletal Class II craniofacial phenotypes in Yemeni adults. These phenotypes differed primarily in mandibular retrusion severity, vertical skeletal pattern and dentoalveolar compensation, and they mapped onto different clinical management pathways ranging from growth modification and camouflage to temporary anchorage device-assisted open-bite correction and orthognathic surgery. This phenotypic framework emphasises the heterogeneity of Class II malocclusion, supports more individualised treatment planning and provides population-specific reference data for Yemeni adults. It also lays the groundwork for future genetic and artificial-intelligence-assisted studies aimed at improving diagnostic precision and treatment outcomes. Abbreviations AOB anterior open bite ANB A–point–Nasion–B–point angle CBCT cone–beam computed tomography FOV field of view ICC intraclass correlation coefficient PCA principal component analysis PC principal component RMS root mean square TAD temporary anchorage device TMJ temporomandibular joint Declarations CBCT scans were obtained only when clinically indicated as part of routine orthodontic diagnosis, and acquisition followed ALARA principles aligned with AAOMR recommendations. Ethics approval and consent to participate Approved by the Medical Ethics Committee, Faculty of Dentistry, Sana’a University, Yemen (OR:19/11/2023). Written informed consent for imaging and secondary analysis was obtained from all participants. Consent for publication Not applicable; no identifiable images or personal data are included. Funding This research received no external funding. Author Contribution SMH conceived the study, collected and analysed the data and drafted the manuscript. RI supervised the study, contributed to study design and interpretation of results and critically revised the manuscript. Both authors read and approved the final manuscript. Acknowledgements The author thanks colleagues at the Faculty of Dentistry, Sana’a University for access to clinical records. Data Availability De‑identified measurement data, principal‑component loadings, and cluster labels will be shared upon reasonable request to the corresponding author and can be deposited to an open repository upon acceptance. References Proffit WR, Fields HW, Larson BE, Sarver DM. Contemporary Orthodontics. 6th ed. St. Louis: Elsevier; 2018. Angle EH. Classification of malocclusion. Dent Cosmos. 1899;41:248–64. Jacobson A. The Wits appraisal of jaw disharmony. Am J Orthod. 1975;67(2):125–38. PMID: 1055014. Moyers RE. Handbook of Orthodontics. 4th ed. Chicago: Year Book Medical; 1988. Broadbent BH. A new x-ray technique and its application to orthodontia. Angle Orthod. 1931;1(2):45–66. 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AI-based automated orthodontic diagnosis. Angle Orthod. 2022;92(4):537–44. PMID: 35143561. Martinez A et al. Integration of AI with CBCT phenotyping: a narrative review. Prog Orthod. 2022;23:15. PMID: 35568111. Chen S, et al. Cephalometric AI systems: clinical validation and limitations. Orthod Craniofac Res. 2023;26(1):1–11. PMID: 36478662. Hafedh SM, et al. Maxilla and mandible bone density in Yemeni adults: CBCT study. Sana’a Univ J Med Health Sci. 2025;19(2):170–75. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8367021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562486361,"identity":"7c6b6c49-cb5b-4fb4-afa4-fd23b29d272d","order_by":0,"name":"Salah M. 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Bin","lastName":"Hafedh","suffix":""},{"id":562486362,"identity":"febdcc86-08b6-4e78-bd73-e4a919f47caa","order_by":1,"name":"Ramy Ishaq","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Ramy","middleName":"","lastName":"Ishaq","suffix":""}],"badges":[],"createdAt":"2025-12-15 13:54:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8367021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8367021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98780358,"identity":"c3cf6eb2-c443-4e23-a77e-bb526ccd1107","added_by":"auto","created_at":"2025-12-22 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12:31:33","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4244160,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/36995add89a380ab45af371b.tif"},{"id":98766335,"identity":"d77b192e-66cc-4871-b529-aad6a99b3088","added_by":"auto","created_at":"2025-12-22 10:15:43","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5571650,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/46398bcfc6b2f65ae59f880d.tif"},{"id":98766333,"identity":"5bbbd1ec-c5a2-41e9-b2e8-b3491e138a1f","added_by":"auto","created_at":"2025-12-22 10:15:43","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137060,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4heatmapclustermeans.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/f957c9a18c75b3c0b27ebd6c.png"},{"id":98779817,"identity":"02ecf6f5-b4d8-4160-9c83-458cfec16da0","added_by":"auto","created_at":"2025-12-22 12:30:47","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51103,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4heatmapclustermeans.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/c637e5173c59aa775cbde1a3.png"},{"id":98766350,"identity":"64b5ad1d-b577-44ea-8f2c-bc032fd43ce4","added_by":"auto","created_at":"2025-12-22 10:15:43","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100465,"visible":true,"origin":"","legend":"","description":"","filename":"d7f5d95efdbf48b9a51846747ef170f01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/3286275c997a23d5e4d699ed.xml"},{"id":98766338,"identity":"89359d7f-ceb1-4b42-a1af-10ccc9ad61ab","added_by":"auto","created_at":"2025-12-22 10:15:43","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112332,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/1e686bf6b71d091ac4fdf201.html"},{"id":98780115,"identity":"049ec225-ef37-4aac-926e-22575913de29","added_by":"auto","created_at":"2025-12-22 12:31:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39709,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of principal components. Eigenvalues plotted against component number; an elbow supports retention of seven components that collectively explain 60.2% of variance in the 63‑variable dataset. Abbreviations: PC, principal component.\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/59462ccf41d506befc4c4210.png"},{"id":98780792,"identity":"06b6cec5-2f0e-4cb6-9b91-20dd1adc2912","added_by":"auto","created_at":"2025-12-22 12:31:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41263,"visible":true,"origin":"","legend":"\u003cp\u003e3D scatter of clusters in PC space. Each point is a subject (n = 120) shown by scores on PC1–PC3; colors denote the five k‑means clusters derived from all retained PC scores. Axes are standardized.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/9afbc9f642f8d1bf57e4c347.png"},{"id":98766337,"identity":"16d868c0-84f7-49a9-b393-d7e6832504bd","added_by":"auto","created_at":"2025-12-22 10:15:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26780,"visible":true,"origin":"","legend":"\u003cp\u003eCluster sample sizes. Bar chart showing the number of subjects per cluster (five‑cluster solution). Counts reflect the final clustering labels used in the analysis.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/ec3143048265be1b3277fa36.png"},{"id":98766325,"identity":"0a066d3f-1b23-4f32-a97a-8eeedad9b492","added_by":"auto","created_at":"2025-12-22 10:15:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137060,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of cluster-level cephalometric means. Colour-coded heatmap showing the mean values of key skeletal and dentoalveolar variables across the five CBCT-derived Class II phenotypic clusters (see Table 6 for numerical values). Warmer colours indicate higher mean values. SNA, sella–nasion to A-point angle; SNB, sella–nasion to B-point angle; ANB, A-point–Nasion–B-point angle; AO–BO, Wits appraisal; SN-GoGn, mandibular plane angle; FH-MP, Frankfort–mandibular plane angle; U1–SN, maxillary incisor inclination to SN; L1–Mp (L1–MP), mandibular incisor inclination to mandibular plane; Co-A, maxillary unit length; Co-Gn, mandibular unit length; N-ANS, upper anterior facial height; ANS-Me, lower anterior facial height; U1-PP, maxillary incisor vertical position.\u003c/p\u003e","description":"","filename":"Figure4heatmapclustermeans.png","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/f5bc0b46e7723836f2066772.png"},{"id":98797867,"identity":"e280c547-eef4-4df0-8802-d2b101064404","added_by":"auto","created_at":"2025-12-22 13:59:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8367021/v1/622d0b5e-3d0b-4f2e-a2d1-33a5e6897073.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CBCT-based three-dimensional phenotyping of skeletal Class II malocclusion in Yemeni adults: principal components and cluster analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eSkeletal Class II malocclusion is among the most common orthodontic problems and is characterised by considerable heterogeneity in sagittal, vertical and dentoalveolar relationships. This heterogeneity contributes to variability in treatment needs and outcomes, particularly in borderline cases where clinicians must choose between growth modification, orthodontic camouflage and orthognathic surgery. Conventional two-dimensional (2D) cephalometry remains widely used but is limited by projection errors, superimposition and an inability to represent complex three-dimensional (3D) relationships. Cone-beam computed tomography (CBCT) provides isotropic 3D data and allows more accurate assessment of skeletal, dental, airway and temporomandibular joint structures when justified by clinical indications. CBCT has been used to evaluate condylar morphology, tongue and hyoid position, periodontal support and cranial base morphology in various malocclusions, including Class II. However, there is a paucity of population-specific 3D phenotypic data for Middle Eastern and Yemeni populations, despite evidence that malocclusion profiles, early tooth loss and deleterious oral habits differ from those in Western cohorts. Beyond single-variable analysis, multivariate approaches such as principal component analysis (PCA) and cluster analysis (CA) allow dimensionality reduction and identification of clinically coherent sub-phenotypes. Recent CBCT-based work in skeletal Class III malocclusion and facial asymmetry has shown that PCA/CA can reveal distinct craniofacial patterns with implications for diagnosis and surgical planning. This study builds on previous thesis work that systematically characterised Class II phenotypes in Yemeni adults using CBCT-derived measurements, PCA and CA, and reframes those findings to emphasise their clinical implications. [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eObjectives\u003c/p\u003e \u003cp\u003eThis study aimed to: (1) use PCA to reduce 63 CBCT variables into a smaller number of principal components describing major dimensions of variation in skeletal Class II malocclusion in Yemeni adults; (2) apply K-means cluster analysis to derive homogeneous phenotypic subgroups; and (3) discuss the clinical implications of these CBCT-derived phenotypes for individualised treatment strategies, particularly with respect to growth modification, orthodontic camouflage and open-bite management with temporary anchorage devices or orthognathic surgery.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy design and ethical approval\u003c/p\u003e\u003cp\u003eThis cross-sectional observational study was conducted at the Faculty of Dentistry, Sana’a University, and collaborating private orthodontic practices in Sana’a, Yemen. CBCT scans were obtained from routine pre-treatment records of patients seeking orthodontic care. The study protocol was approved by the Medical Ethics Committee, Faculty of Dentistry, Sana’a University (Approval code: OR:19/11/2023), in accordance with the Declaration of Helsinki. Written informed consent for diagnostic imaging and secondary use of anonymised data was obtained from all participants at the time of CBCT acquisition.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThe sample comprised 120 Yemeni adults with skeletal Class II malocclusion (64 females, 53.3%; 56 males, 46.7%) aged 16–30 years. Inclusion criteria were: Yemeni nationality; age 16–30 years; skeletal Class II malocclusion defined as ANB ≥ 4° and Wits appraisal greater than 2 mm; at least unilateral Angle Class II molar or canine relationship with a convex soft-tissue profile; full permanent dentition excluding third molars; and availability of high-quality pre-treatment CBCT records. Exclusion criteria were: craniofacial syndromes or cleft lip/palate; severe facial asymmetry; history of significant facial trauma, temporomandibular joint disorders or systemic diseases affecting bone metabolism; missing teeth other than third molars or impacted/blocked-out teeth; and poor-quality or incomplete CBCT scans. These criteria were chosen to minimise heterogeneity and to ensure that the observed variation primarily reflected phenotypic differences in skeletal Class II malocclusion. Sample characteristics are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size, n\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range, years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16–30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (53.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (46.7)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal Class II (ANB ≥ 4°, Wits \u0026gt; 2 mm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (100)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least unilateral Angle Class II molar/canine relation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (100)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConvex soft-tissue profile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (100)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCBCT acquisition\u003c/p\u003e\u003cp\u003eAll CBCT scans were acquired using a PaX-Flex 3D P2 unit (Vatech, Korea) under a standardised protocol: field of view 15 × 15 cm, isotropic voxel size 0.3 mm, 85–90 kV, 10 mA and exposure time approximately 17 s. Patients were positioned in natural head posture with the Frankfort horizontal plane parallel to the floor and the midsagittal plane perpendicular to the floor. Teeth were in maximum intercuspation and lips were relaxed. A lead apron was used for radiation protection, and all scans were taken by the same experienced radiology technician to ensure reproducibility. DICOM datasets were exported and imported into Invivo 6.0 (Anatomage, San Jose, CA, USA) for 3D reconstruction, landmark identification and measurement. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eLandmark identification and measurements\u003c/p\u003e\u003cp\u003eThirty craniofacial landmarks (skeletal, dental and selected soft-tissue points) were identified in three planes of space. Landmarks were chosen to capture cranial base length and angulation; maxillary and mandibular position and lengths; vertical dimensions (anterior and posterior facial height, mandibular plane angles); maxillo-mandibular relationships (overjet, overbite, Wits-equivalent measures); incisor position and inclination; and selected airway/soft-tissue parameters where visible. Bilateral landmarks were traced on both sides and averaged to reduce the influence of minor asymmetry. From these landmarks, 63 three-dimensional variables were computed, including linear, angular and ratio measures, providing comprehensive coverage of cranial base, maxillary, mandibular, intermaxillary and dental/soft-tissue dimensions. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eReliability assessment\u003c/p\u003e\u003cp\u003eTo assess intra- and inter-examiner reliability, 20 CBCT scans were randomly selected. All landmarks were traced by the primary examiner and re-traced after a two-week interval, and independently traced once by a second examiner. Intraclass correlation coefficients exceeded 0.85 for all variables, indicating excellent reliability; no systematic differences were detected on paired t-tests or Wilcoxon signed-rank tests (p \u0026gt; 0.05).\u003c/p\u003e\u003cp\u003ePrincipal component analysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eContinuous variables were checked for normality using the Shapiro–Wilk test and inspection of histograms and Q–Q plots. Data were standardised (z-scores) and analysed with IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). Principal component analysis (PCA) with varimax rotation and Kaiser normalisation was applied to the 63 variables. Components with eigenvalues greater than 1 were retained. The number of components was determined by eigenvalues and scree plot inspection, supported by clinical interpretability. Variables with loadings ≥ |0.50| were considered meaningful contributors to each component, and component scores were saved for subsequent cluster analysis.\u003c/p\u003e\u003cp\u003eCluster analysis and statistical testing [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eK-means cluster analysis with Euclidean distance was performed on the retained component scores. Solutions with k ranging from 3 to 7 were examined. The final five-cluster solution was selected based on stability across random initialisations, proportion of variance explained and clinical interpretability of the resulting phenotypes. Canonical discriminant analysis was used to visualise inter-cluster separation and to inspect for outliers using prediction ellipses. One-way ANOVA (or Kruskal–Wallis tests where appropriate) was used to compare key skeletal and dental variables among clusters, with post-hoc pairwise comparisons adjusted for multiple testing. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePrincipal components\u003c/p\u003e \u003cp\u003eSeven principal components with eigenvalues greater than 1 were retained, together explaining 60.2% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PC1 had an eigenvalue of 4.275 and accounted for 10.2% of the variance; PC2, PC3 and PC4 explained 9.3%, 9.1% and 8.8%, respectively, and PCs 5\u0026ndash;7 each contributed between 5.8% and 8.7%. PC1 primarily reflected vertical skeletal pattern and cranial base length, with high positive loadings on mandibular plane angle, lower anterior facial height and cranial base length. PC2 predominantly represented maxillary incisor inclination and dentoalveolar compensation; PC3 differentiated shorter versus longer mandibles and associated posterior facial height; and PC4 related mainly to maxillary anteroposterior position and unit length. PCs 5\u0026ndash;7 represented smaller proportions of variance and corresponded to mandibular incisor position, facial taper and the severity of sagittal discrepancy. The scree plot confirmed a clear elbow at seven components, and a 95% prediction ellipse of component scores showed no statistical outliers (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative variance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster solution and sample distribution\u003c/p\u003e \u003cp\u003eK-means clustering on the seven component scores yielded a five-cluster solution that balanced parsimony with clinical interpretability. Canonical discriminant plots showed good separation between clusters with minimal overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cluster sizes and distances between centroids are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Cluster 1 contained 42 subjects (35.0%), cluster 2 contained 24 (20.0%), and clusters 3, 4 and 5 each contained 18 subjects (15.0%). Root mean square distances within clusters ranged from 0.243 to 0.340, and inter-centroid distances exceeded 2.40, indicating well-separated phenotypic groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of sample (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoot mean square (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNearest cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDistance between centroids\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePhenotypic characterisation of clusters\u003c/p\u003e \u003cp\u003eBased on component loadings and cluster centroids, the five clusters were characterised as follows. Cluster 1 represented subjects with a relatively long cranial base, mild retrusion of both maxilla and mandible, a normodivergent vertical pattern and increased overjet with deep-bite tendency. Cluster 2 showed a normal cranial base with predominantly mandibular retrusion, mildly decreased mandibular plane angle (hypodivergent pattern) and normal overbite or mild deep bite. Cluster 3 comprised individuals with a short cranial base and mandibular body, more pronounced mandibular retrusion, reduced posterior facial height and a deep-bite tendency with marked overjet. Cluster 4 exhibited a short cranial base, maxillary protrusion combined with mandibular retrusion, steep mandibular plane angle, reduced ramus height, increased facial taper and an anterior open-bite tendency or low overbite with greater inter-labial gap. Cluster 5 included subjects with a normal cranial base, mildly protrusive maxilla and mildly retrusive mandible, significantly reduced mandibular plane angle (flat mandibular plane), protrusive maxillary incisors, normal mandibular incisors and normal or slightly reduced overbite with increased overjet and shorter anterior facial height. Across clusters, one-way ANOVA revealed significant differences in key skeletal and dental variables, including mandibular plane angle, posterior facial height, ANB, maxillary and mandibular unit lengths, and incisor inclinations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additional cluster-level cephalometric means for all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 2 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster 3 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster 4 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCluster 5 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandibular plane angle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e34.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e38.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e48.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior facial height (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANB (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaxillary unit length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e76.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e76.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e75.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e70.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandibular unit length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e96.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e98.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e96.26\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e100.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e97.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverjet (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverbite (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU1\u0026ndash;SN (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e124.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e120.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e102.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e123.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e115.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1\u0026ndash;MP (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e96.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e96.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e96.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e96.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e96.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1 Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 1 SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster 2 Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster 2 SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCluster 3 Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCluster 3 SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCluster 4 Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCluster 4 SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCluster 5 Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCluster 5 SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eANOVA p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e75.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.05299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.9284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAO-BO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.03e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN-GoGn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e48.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.531e-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFH-MP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.227e-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU1 - SN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e123.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e115.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.352e-43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1 - Mp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.08302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e70.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.439e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-Gn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e97.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-ANS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e47.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.953e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANS-Me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e65.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.656e-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU1-PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e27.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.532e-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1-MP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e96.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.9847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical mapping of phenotypes\u003c/p\u003e \u003cp\u003eThe identified clusters aligned with clinically recognisable treatment categories. Clusters 1\u0026ndash;3, which were mostly normo- or hypodivergent with deep-bite tendencies, appear suitable for growth-modification approaches in growing patients and orthodontic camouflage in adults, with emphasis on sagittal correction and bite opening. Cluster 4, a hyperdivergent phenotype with open-bite tendency, requires intensive vertical control, often with temporary anchorage devices for posterior intrusion and, in more severe cases, combined orthodontic\u0026ndash;orthognathic surgery. Cluster 5, a hypodivergent phenotype with protrusive maxillary incisors, can be managed with careful anchorage planning and torque control to address incisor proclination and overjet while maintaining favourable vertical relationships. A qualitative clinical summary of each cluster and suggested treatment directions is presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey phenotypic features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypical clinical concerns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredominant treatment direction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetropositioned maxilla and mandible; long cranial base; deep bite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduced facial convexity; deep overbite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrowth modification or camouflage; sagittal advancement and bite opening\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandibular retrusion; hypodivergent; normal overbite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChin deficiency; possible crowding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunctional appliances in growing patients; camouflage or surgery in adults\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShort cranial base and mandibular body; reduced posterior facial height; deep bite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarked overjet and deep overbite; short lower face\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBite-opening mechanics; torque control; possible extractions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaxillary protrusion plus mandibular retrusion; steep mandibular plane; open-bite tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncreased lower facial height; anterior open bite risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVertical control with TADs; orthognathic surgery in severe cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat mandibular plane; mild maxillary protrusion; protrusive maxillary incisors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncreased overjet; incisor proclination/gingival risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnchorage-reinforced mechanics; torque control; selective extractions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis CBCT-based multivariate analysis identified seven principal components and five distinct skeletal Class II phenotypes in Yemeni adults. The components captured major dimensions of vertical pattern, cranial base morphology, jaw length and position, and incisor inclination, while the clusters integrated these dimensions into clinically recognisable subgroups. Mandibular retrusion was a common feature across most clusters, but vertical pattern and dentoalveolar compensation varied substantially, supporting the concept that Class II malocclusion is a heterogeneous spectrum rather than a single entity. The presence of a hyperdivergent, open-bite-prone cluster (cluster 4) is particularly relevant clinically, as such patients often require rigorous vertical control, temporary anchorage device-supported mechanics and, in severe cases, orthognathic surgery. Hypodivergent clusters with deep bite (clusters 2 and 5) are generally more amenable to growth modification or camouflage, though careful control of incisor torque and periodontal support is crucial, especially when significant retraction is planned. The phenotypic clusters described here broadly align with patterns reported in previous phenotyping studies using PCA and cluster analysis in skeletal Class III malocclusion and facial asymmetry, while adding novel information about skeletal Class II variation in a Yemeni adult population. A major strength of this study is the use of 3D CBCT data and a comprehensive variable set, combined with robust multivariate modelling and stringent inclusion criteria. However, the clinic-based, cross-sectional design limits generalisability to the wider Yemeni population and precludes conclusions about growth or long-term stability within each phenotype. Determining the optimal number of clusters also involves clinical judgement, even when supported by statistical criteria, and alternative partitions might be defensible. In addition, treatment recommendations are inferential, based on mapping clusters to findings from external treatment studies, rather than on prospective outcome data for each cluster. Future research should validate these phenotypes in independent samples and other ethnic groups, stratify Class II into divisions 1 and 2 to explore potential differences, and prospectively assess treatment outcomes and stability by cluster. Integration of CBCT-based phenotyping with artificial intelligence tools and genetic analyses may further advance precision orthodontics. [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCBCT-based PCA and cluster analysis revealed five distinct skeletal Class II craniofacial phenotypes in Yemeni adults. These phenotypes differed primarily in mandibular retrusion severity, vertical skeletal pattern and dentoalveolar compensation, and they mapped onto different clinical management pathways ranging from growth modification and camouflage to temporary anchorage device-assisted open-bite correction and orthognathic surgery. This phenotypic framework emphasises the heterogeneity of Class II malocclusion, supports more individualised treatment planning and provides population-specific reference data for Yemeni adults. It also lays the groundwork for future genetic and artificial-intelligence-assisted studies aimed at improving diagnostic precision and treatment outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanterior open bite\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eA\u0026ndash;point\u0026ndash;Nasion\u0026ndash;B\u0026ndash;point angle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econe\u0026ndash;beam computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efield of view\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintraclass correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eroot mean square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etemporary anchorage device\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etemporomandibular joint\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003eCBCT scans were obtained only when clinically indicated as part of routine orthodontic diagnosis, and acquisition followed ALARA principles aligned with AAOMR recommendations.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Approved by the Medical Ethics Committee, Faculty of Dentistry, Sana\u0026rsquo;a University, Yemen (OR:19/11/2023). Written informed consent for imaging and secondary analysis was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable; no identifiable images or personal data are included.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSMH conceived the study, collected and analysed the data and drafted the manuscript. RI supervised the study, contributed to study design and interpretation of results and critically revised the manuscript. Both authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author thanks colleagues at the Faculty of Dentistry, Sana\u0026rsquo;a University for access to clinical records.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDe‑identified measurement data, principal‑component loadings, and cluster labels will be shared upon reasonable request to the corresponding author and can be deposited to an open repository upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eProffit WR, Fields HW, Larson BE, Sarver DM. Contemporary Orthodontics. 6th ed. St. Louis: Elsevier; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngle EH. Classification of malocclusion. Dent Cosmos. 1899;41:248\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobson A. The Wits appraisal of jaw disharmony. Am J Orthod. 1975;67(2):125\u0026ndash;38. PMID: 1055014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoyers RE. Handbook of Orthodontics. 4th ed. Chicago: Year Book Medical; 1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroadbent BH. A new x-ray technique and its application to orthodontia. Angle Orthod. 1931;1(2):45\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteiner CC. Cephalometrics in clinical practice. Angle Orthod. 1953;19(3):97\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowns WB. Variations in facial relationships: their significance in treatment and prognosis. Am J Orthod. 1948;34(10):812\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnlow DH, Hans MG. Essentials of Facial Growth. Philadelphia: WB Saunders; 1996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaccetti T, Franchi L, McNamara JA. Growth in the untreated Class II subject. Semin Orthod. 2006;12(2):98\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePancherz H. The Herbst appliance\u0026mdash;its biologic effects and clinical use. Am J Orthod. 1985;87(1):1\u0026ndash;20. PMID: 3855433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUribe LM, Howe SC, Kummet C, Vela KC, Dawson DV, Southard TE. Phenotypic diversity in white adults with moderate to severe Class II malocclusion. Am J Orthod Dentofac Orthop. 2014;145(3):305\u0026ndash;16. PMID: 24582022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaig N, et al. Artificial intelligence in orthodontic diagnosis: current applications and future perspectives. Semin Orthod. 2021;27(3):199\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBor S, et al. Deep learning-based cephalometric landmark detection: systematic review. Orthod Craniofac Res. 2022;25(4):473\u0026ndash;85. 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Sana\u0026rsquo;a Univ J Med Health Sci. 2025;19(2):170\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Class II malocclusion, cone‑beam computed tomography, principal component analysis, cluster analysis, craniofacial phenotype, orthodontic treatment planning","lastPublishedDoi":"10.21203/rs.3.rs-8367021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8367021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo reduce 63 CBCT variables into principal components (PCs) describing major dimensions of skeletal Class II variation in Yemeni adults, derive phenotypic subgroups by clustering, and outline treatment implications.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003ePretreatment CBCT scans of 120 adults (16\u0026ndash;30 years) with skeletal Class II were analyzed. Variables were z‑standardized; PCA with varimax rotation retained components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1. K‑means clustering on PC scores yielded phenotypes. Between‑cluster differences used ANOVA/Kruskal\u0026ndash;Wallis (α\u0026thinsp;=\u0026thinsp;0.05). Reliability was assessed using intraclass correlation coefficients (ICC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven PCs explained 60.2% of total variance. Five clusters with distinct skeletal and dentoalveolar patterns were identified; key variables differed significantly among clusters. ICCs exceeded 0.85 across variables, indicating excellent measurement reliability.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCBCT‑based PCA and clustering uncovered five clinically coherent Class II phenotypes that map to different management pathways (e.g., growth modification or camouflage for hypodivergent patterns; vertical control/TADs and, in severe cases, combined orthodontic\u0026ndash;orthognathic care for hyperdivergent open‑bite\u0026ndash;prone patterns).\u003c/p\u003e\u003ch2\u003eClinical relevance\u003c/h2\u003e \u003cp\u003eMultivariate CBCT phenotyping clarifies heterogeneity in Class II malocclusion and can guide case‑specific mechanics and surgery decisions in adult patients.\u003c/p\u003e","manuscriptTitle":"CBCT-based three-dimensional phenotyping of skeletal Class II malocclusion in Yemeni adults: principal components and cluster analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:15:34","doi":"10.21203/rs.3.rs-8367021/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7dcea1dc-ce91-422f-beb1-15dac1fd91da","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T12:30:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:15:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8367021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8367021","identity":"rs-8367021","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}