CBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI Workflow

CBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI Workflow | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI Workflow Dr Salah M. Ben Hafedh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8049618/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 Background: Cone-beam computed tomography (CBCT) enables three-dimensional assessment of craniofacial structures; however, translating multiple inter‑correlated measurements into diagnostic insight remains challenging. Objective: To present an explainable engineering workflow combining principal component analysis (PCA) and K‑means clustering to identify skeletal Class II phenotypes in adults. Methods: Sixty‑three CBCT variables from 120 Yemeni adults were standardized, reduced via PCA, and clustered (k = 2–8). Internal validity was evaluated with silhouette and Davies–Bouldin indices and stability across multiple random starts; phenotypes were mapped to clinical strategies. Results: Seven components explained ≈60% of variance. A five‑cluster solution balanced cohesion, separation, and clinical interpretability, delineating deep‑bite and open‑bite tendencies, mandibular retrusion patterns, and incisor‑protrusive phenotypes. Solutions were stable across seeds and simple demographic strata. Conclusions: The PCA→K‑means pipeline provides a transparent, reproducible framework for AI‑assisted orthodontic diagnosis and phenotype‑based treatment planning, aligning with biomedical imaging and radiomics workflows. Dentistry CBCT 3-D imaging radiomics explainable AI principal component analysis K‑means clustering phenotyping orthodontic diagnostics Figures Figure 1 Figure 2 Figure 3 1. Introduction Cone‑beam computed tomography (CBCT) has transformed orthodontic imaging by providing 3‑D datasets with isotropic voxels and high geometric fidelity, enabling precise linear and angular assessments across craniofacial regions. Yet, clinically collected CBCT variables are numerous and correlated, complicating etiologic reasoning and treatment planning. From a bioengineering perspective, dimensionality reduction with PCA preserves multivariate structure while K‑means clustering provides an interpretable partitioning of patient phenotypes using Euclidean geometry and centroid summaries. We apply a transparent PCA→K‑means pipeline to adults with skeletal Class II malocclusion and report components, clusters, and phenotype‑specific clinical implications. To align with the Diagnostic Imaging and Radiomics focus, we emphasize explainability, reproducibility, and the potential to integrate into AI‑ready imaging workflows. [ 7 – 9 ] [ 1 – 3 ] [ 6 , 11 , 12 ] 2. Materials and Methods 2.1 Study Design and Participants This retrospective observational study evaluated 120 Yemeni adults diagnosed with skeletal Class II malocclusion. Inclusion criteria comprised complete CBCT records, adult dentition, and absence of syndromic conditions, craniofacial trauma, or prior orthognathic surgery. Exclusion criteria were poor image quality, artifacts interfering with landmarking, and missing key variables. 2.2 Imaging Protocol and Measurements CBCT scans were acquired on a PaX‑Flex 3D P2 system with a medium field of view. Landmarks and measurements were extracted in Invivo 6.0 following standardized operating procedures. Sixty‑three variables captured sagittal, vertical, and transverse indicators, as well as maxillary and mandibular incisor inclinations and positions. [ 7 – 9 , 15 ] 2.3 Preprocessing and Dimensionality Reduction Variables were screened for completeness, standardized to z‑scores, and subjected to PCA on the correlation matrix. Component retention balanced eigenvalue > 1, visual inspection of the scree profile, cumulative variance criteria, and anatomical interpretability of loadings. [ 1 – 3 , 10 ] 2.4 Unsupervised Clustering K‑means clustering was performed on retained PCA scores using Euclidean distance. Candidate solutions for k ranging from 2 to 8 were fitted with multiple random initializations to avoid local minima. The smallest k exhibiting strong cohesion/separation and clinical interpretability was selected. [ 6 , 11 , 12 ] 2.5 Cluster Validity and Sensitivity Analyses Silhouette coefficients and Davies–Bouldin indices quantified within‑cluster cohesion and between‑cluster separation. Robustness was assessed across seeds and simple stratifications (e.g., sex). [ 4 , 5 ] 2.6 Statistical Environment and Reproducibility Analyses followed reproducible steps implementable in common environments (e.g., Python/R). The pipeline is intentionally simple—standardization, PCA on the correlation matrix, K‑means on retained scores, and validity indices—facilitating multi‑center replication. [ 1 – 3 ] 3. Results 3.1 Component Loadings and Anatomical Interpretation Seven principal components captured approximately 60% of variance. PC1 emphasized cranial‑base length; PC2/PC3 captured variance in mandibular plane steepness and facial height; PC4 reflected maxillary sagittal position with secondary contributions from incisor inclination. [ 7 – 9 , 13 – 15 ] 3.2 Cluster Centroids and Clinical Readouts K‑means on retained scores yielded a five‑cluster solution balancing internal validity and clinical interpretability. Centroid inspection clarified actionable differences: deep‑bite clusters favored torque control and bite‑opening mechanics; open‑bite clusters benefited from vertical control; mandibular retrusion clusters mapped to growth modification or orthognathic pathways depending on age; incisor‑protrusive patterns called for anchorage planning and torque adjustments. [ 4 , 5 ] 3.3 Sensitivity to Component Retention and k‑Selection Retaining fewer than seven components modestly reduced silhouette profiles, while adding components beyond seven increased within‑cluster variance without improving separation. k = 4 merged distinct vertical phenotypes, whereas k = 6 split a stable centroid into two near duplicates; thus, k = 5 offered the best balance of parsimony and interpretability. [ 4 , 5 ] 3.4 Visualization Two‑dimensional score plots (PC1 vs. PC2) illustrated separable but partially overlapping cloud structures consistent with realistic craniofacial heterogeneity. The workflow and scree/cluster plots are provided as Figs. 1–3. 4. Discussion This work complements deep‑learning efforts in dental imaging by focusing on unsupervised phenotyping and transparent dimensionality reduction, which are essential for explainable decision support. Internal validity indices supported k = 5 and phenotypes were clinically interpretable—a practical criterion often overlooked by purely performance‑driven optimization. Generalizability remains a limitation because the cohort represents Yemeni adults; multi‑center validation across devices/software is warranted. Beyond morphology, integration with outcomes and patient‑reported measures would support phenotype‑to‑protocol pathways and personalized biomechanics. [ 16 – 18 , 20 ] 5. Conclusions A compact PCA→K‑means pipeline converts high‑dimensional CBCT variables into reproducible skeletal Class II phenotypes suitable for radiomics and clinical workflows. The approach emphasizes dimensionality reduction, unsupervised learning, and objective validation and can be deployed across multi‑center datasets to advance explainable, AI‑assisted orthodontic diagnostics. 5.1 Practical Checklist for Clinical Deployment • Governance: Verify IRB oversight and de‑identification. • Protocols: Fix acquisition presets and document scanner settings. • Measurement: Calibrate operators; perform periodic ICC checks. • Analytics: Standardize variables; retain components by elbow + interpretability; use multi‑start K‑means; report silhouette/Davies–Bouldin indices. • Translation: Maintain phenotype templates mapping centroids to orthodontic strategies; monitor outcomes. • Safety: Apply ALARA/ALADA principles; avoid unnecessary volumetric imaging. Declarations Institutional Review Board Statement This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Sana’a University, Yemen (Approval ID: OR 19/11/2023). Informed Consent Statement Patient consent was waived due to the retrospective design and de‑identified nature of the dataset. Data Availability Statement Data are available from the corresponding author upon reasonable request due to ethical restrictions and de‑identification. Author Contributions Conceptualization, S.M.B.H.; methodology, S.M.B.H., M.K.J., and M.H.A.-Q.; software/visualization, S.M.B.H.; formal analysis, S.M.B.H.; resources, M.K.J. and M.H.A.-Q.; data curation, S.M.B.H.; writing—original draft, S.M.B.H.; writing—review and editing, S.M.B.H., M.K.J., and M.H.A.-Q.; supervision, S.M.B.H. All authors approved the submitted version. Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest. Acknowledgments We thank the Medical Ethics Committee of Sana’a University for oversight and Genesis Medical & Cosmetics Company for administrative support. References Abdi H, Williams LJ. Principal component analysis. WIREs Comput Stat. 2010;2(4):433–59. https://doi.org/10.1002/wics.101 Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202 Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. 2nd ed. Springer; 2009. https://link.springer.com/book/10.1007/978-0-387-84858-7 Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7 Davies DL, Bouldin DW. A cluster separation measure. IEEE TPAMI. 1979;1:224–27. https://doi.org/10.1109/TPAMI.1979.4766909 Hartigan JA, Wong MA. A K‑Means clustering algorithm. J R Stat Soc C. 1979;28(1):100–8. https://doi.org/10.2307/2346830 Scarfe WC, Farman AG. What is cone‑beam CT and how does it work? Dent Clin North Am. 2008;52(4):707–30. https://doi.org/10.1016/j.cden.2008.05.005 Venkatesh E, Elluru SV. Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent. 2017;51(Suppl 1):S102–21. https://pmc.ncbi.nlm.nih.gov/articles/PMC5750833/ Pauwels R, Beinsberger J, Collaert B, et al. Effective dose range for dental CBCT scanners. Eur J Radiol. 2012;81(2):267–71. https://doi.org/10.1016/j.ejrad.2010.11.028 Izenman AJ. Modern Multivariate Statistical Techniques. Springer; 2008. https://link.springer.com/book/10.1007/978-0-387-78189-1 MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proc 5th Berkeley Symp Math Stat Prob. 1967:281–97. https://projecteuclid.org/journals/proceedings-of-the-fifth-berkeley-symposium-on-mathematical-statistics-and-probability/volume-1/issue-1/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992.full Lloyd SP. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129–37. https://doi.org/10.1109/TIT.1982.1056489 Damstra J, Fourie Z, Huddleston Slater JJ, Ren Y. Accuracy of linear measurements from CBCT‑derived 3‑D surface models. Am J Orthod Dentofacial Orthop. 2010;137(4):S50–S1. https://pubmed.ncbi.nlm.nih.gov/20122425/ Gribel BF, Gribel MN, Manzi FR, Brooks SL, McNamara JA Jr. Accuracy and reliability of craniometric measurements on lateral cephalograms vs CBCT. Angle Orthod. 2011;81(1):26–35. https://media.dent.umich.edu/labs/mcnamara/files/Accuracy%20and%20reliability%20of%20craniometric%20measurements%20on%20lateral%20cephalograms%20and%20CBCT.pdf Scarfe WC, Aboelsaad N, Farman AG, et al. CBCT in orthodontics. Aust Dent J. 2017;62(S1):33–50. https://onlinelibrary.wiley.com/doi/abs/10.1111/adj.12479 Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769–74. https://doi.org/10.1177/0022034520915714 Arsiwala‑Scheppach LT, Kronig J, et al. Machine learning in dentistry: a scoping review. J Clin Med. 2023;12(3):937. https://pubmed.ncbi.nlm.nih.gov/36769585/ Schwendicke F, Chaurasia A, Arsiwala‑Scheppach L, et al. Deep learning for cephalometric landmark detection: systematic review and meta‑analysis. Clin Oral Investig. 2021;25:4299–4312. https://link.springer.com/article/10.1007/s00784-021-03990-w Pauwels R, Smeets B, et al. Converting DAP to effective dose for dental CBCT. Physica Medica. 2023;110:102–10. https://www.physicamedica.com/article/S1120-1797(23)00116-3/fulltext Lakhotia S, et al. Machine learning in dentistry: scoping review. PLOS Digit Health. 2025;4(9):e0000940. https://doi.org/10.1371/journal.pdig.0000940 Additional Declarations The authors declare no competing interests. Supplementary Files GraphicalAbstract.png Graphical Abstract. AI‑CBCT phenotyping workflow. 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-8049618","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541106808,"identity":"822273c5-6b93-43c3-bf44-109ddb520c8d","order_by":0,"name":"Dr Salah M. 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AI‑CBCT phenotyping workflow.\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8049618/v1/a9aecf1ea7e69cc1fc7fd514.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI Workflow\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCone‑beam computed tomography (CBCT) has transformed orthodontic imaging by providing 3‑D datasets with isotropic voxels and high geometric fidelity, enabling precise linear and angular assessments across craniofacial regions. Yet, clinically collected CBCT variables are numerous and correlated, complicating etiologic reasoning and treatment planning. From a bioengineering perspective, dimensionality reduction with PCA preserves multivariate structure while K‑means clustering provides an interpretable partitioning of patient phenotypes using Euclidean geometry and centroid summaries. We apply a transparent PCA\u0026rarr;K‑means pipeline to adults with skeletal Class II malocclusion and report components, clusters, and phenotype‑specific clinical implications. To align with the Diagnostic Imaging and Radiomics focus, we emphasize explainability, reproducibility, and the potential to integrate into AI‑ready imaging workflows. [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e\u003cp\u003eThis retrospective observational study evaluated 120 Yemeni adults diagnosed with skeletal Class II malocclusion. Inclusion criteria comprised complete CBCT records, adult dentition, and absence of syndromic conditions, craniofacial trauma, or prior orthognathic surgery. Exclusion criteria were poor image quality, artifacts interfering with landmarking, and missing key variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Imaging Protocol and Measurements\u003c/h2\u003e\u003cp\u003eCBCT scans were acquired on a PaX‑Flex 3D P2 system with a medium field of view. Landmarks and measurements were extracted in Invivo 6.0 following standardized operating procedures. Sixty‑three variables captured sagittal, vertical, and transverse indicators, as well as maxillary and mandibular incisor inclinations and positions. [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Preprocessing and Dimensionality Reduction\u003c/h2\u003e\u003cp\u003eVariables were screened for completeness, standardized to z‑scores, and subjected to PCA on the correlation matrix. Component retention balanced eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1, visual inspection of the scree profile, cumulative variance criteria, and anatomical interpretability of loadings. [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Unsupervised Clustering\u003c/h2\u003e\u003cp\u003eK‑means clustering was performed on retained PCA scores using Euclidean distance. Candidate solutions for k ranging from 2 to 8 were fitted with multiple random initializations to avoid local minima. The smallest k exhibiting strong cohesion/separation and clinical interpretability was selected. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Cluster Validity and Sensitivity Analyses\u003c/h2\u003e\u003cp\u003eSilhouette coefficients and Davies\u0026ndash;Bouldin indices quantified within‑cluster cohesion and between‑cluster separation. Robustness was assessed across seeds and simple stratifications (e.g., sex). [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Environment and Reproducibility\u003c/h2\u003e\u003cp\u003eAnalyses followed reproducible steps implementable in common environments (e.g., Python/R). The pipeline is intentionally simple\u0026mdash;standardization, PCA on the correlation matrix, K‑means on retained scores, and validity indices\u0026mdash;facilitating multi‑center replication. [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Component Loadings and Anatomical Interpretation\u003c/h2\u003e\u003cp\u003eSeven principal components captured approximately 60% of variance. PC1 emphasized cranial‑base length; PC2/PC3 captured variance in mandibular plane steepness and facial height; PC4 reflected maxillary sagittal position with secondary contributions from incisor inclination. [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Cluster Centroids and Clinical Readouts\u003c/h2\u003e\u003cp\u003eK‑means on retained scores yielded a five‑cluster solution balancing internal validity and clinical interpretability. Centroid inspection clarified actionable differences: deep‑bite clusters favored torque control and bite‑opening mechanics; open‑bite clusters benefited from vertical control; mandibular retrusion clusters mapped to growth modification or orthognathic pathways depending on age; incisor‑protrusive patterns called for anchorage planning and torque adjustments. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Sensitivity to Component Retention and k‑Selection\u003c/h2\u003e\u003cp\u003eRetaining fewer than seven components modestly reduced silhouette profiles, while adding components beyond seven increased within‑cluster variance without improving separation. k\u0026thinsp;=\u0026thinsp;4 merged distinct vertical phenotypes, whereas k\u0026thinsp;=\u0026thinsp;6 split a stable centroid into two near duplicates; thus, k\u0026thinsp;=\u0026thinsp;5 offered the best balance of parsimony and interpretability. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Visualization\u003c/h2\u003e\u003cp\u003eTwo‑dimensional score plots (PC1 vs. PC2) illustrated separable but partially overlapping cloud structures consistent with realistic craniofacial heterogeneity. The workflow and scree/cluster plots are provided as Figs.\u0026nbsp;1\u0026ndash;3.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis work complements deep‑learning efforts in dental imaging by focusing on unsupervised phenotyping and transparent dimensionality reduction, which are essential for explainable decision support. Internal validity indices supported k\u0026thinsp;=\u0026thinsp;5 and phenotypes were clinically interpretable\u0026mdash;a practical criterion often overlooked by purely performance‑driven optimization. Generalizability remains a limitation because the cohort represents Yemeni adults; multi‑center validation across devices/software is warranted. Beyond morphology, integration with outcomes and patient‑reported measures would support phenotype‑to‑protocol pathways and personalized biomechanics. [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA compact PCA→K‑means pipeline converts high‑dimensional CBCT variables into reproducible skeletal Class II phenotypes suitable for radiomics and clinical workflows. The approach emphasizes dimensionality reduction, unsupervised learning, and objective validation and can be deployed across multi‑center datasets to advance explainable, AI‑assisted orthodontic diagnostics.\u003c/p\u003e\n\u003ch2\u003e5.1 Practical Checklist for Clinical Deployment\u003c/h2\u003e\n\u003cp\u003e• Governance: Verify IRB oversight and de‑identification.\u003c/p\u003e\n\u003cp\u003e• Protocols: Fix acquisition presets and document scanner settings.\u003c/p\u003e\n\u003cp\u003e• Measurement: Calibrate operators; perform periodic ICC checks.\u003c/p\u003e\n\u003cp\u003e• Analytics: Standardize variables; retain components by elbow + interpretability; use multi‑start K‑means; report silhouette/Davies–Bouldin indices.\u003c/p\u003e\n\u003cp\u003e• Translation: Maintain phenotype templates mapping centroids to orthodontic strategies; monitor outcomes.\u003c/p\u003e\n\u003cp\u003e• Safety: Apply ALARA/ALADA principles; avoid unnecessary volumetric imaging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eInstitutional Review Board Statement\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Sana\u0026rsquo;a University, Yemen (Approval ID: OR 19/11/2023).\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement\u003c/p\u003e\n\u003cp\u003ePatient consent was waived due to the retrospective design and de‑identified nature of the dataset.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request due to ethical restrictions and de‑identification.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, S.M.B.H.; methodology, S.M.B.H., M.K.J., and M.H.A.-Q.; software/visualization, S.M.B.H.; formal analysis, S.M.B.H.; resources, M.K.J. and M.H.A.-Q.; data curation, S.M.B.H.; writing\u0026mdash;original draft, S.M.B.H.; writing\u0026mdash;review and editing, S.M.B.H., M.K.J., and M.H.A.-Q.; supervision, S.M.B.H. All authors approved the submitted version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the Medical Ethics Committee of Sana\u0026rsquo;a University for oversight and Genesis Medical \u0026amp; Cosmetics Company for administrative support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi H, Williams LJ. Principal component analysis. WIREs Comput Stat. 2010;2(4):433\u0026ndash;59. https://doi.org/10.1002/wics.101\u003c/li\u003e\n\u003cli\u003eJolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202\u003c/li\u003e\n\u003cli\u003eHastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. 2nd ed. Springer; 2009. https://link.springer.com/book/10.1007/978-0-387-84858-7\u003c/li\u003e\n\u003cli\u003eRousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53\u0026ndash;65. https://doi.org/10.1016/0377-0427(87)90125-7\u003c/li\u003e\n\u003cli\u003eDavies DL, Bouldin DW. A cluster separation measure. IEEE TPAMI. 1979;1:224\u0026ndash;27. https://doi.org/10.1109/TPAMI.1979.4766909\u003c/li\u003e\n\u003cli\u003eHartigan JA, Wong MA. A K‑Means clustering algorithm. J R Stat Soc C. 1979;28(1):100\u0026ndash;8. https://doi.org/10.2307/2346830\u003c/li\u003e\n\u003cli\u003eScarfe WC, Farman AG. What is cone‑beam CT and how does it work? Dent Clin North Am. 2008;52(4):707\u0026ndash;30. https://doi.org/10.1016/j.cden.2008.05.005\u003c/li\u003e\n\u003cli\u003eVenkatesh E, Elluru SV. Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent. 2017;51(Suppl 1):S102\u0026ndash;21. https://pmc.ncbi.nlm.nih.gov/articles/PMC5750833/\u003c/li\u003e\n\u003cli\u003ePauwels R, Beinsberger J, Collaert B, et al. Effective dose range for dental CBCT scanners. Eur J Radiol. 2012;81(2):267\u0026ndash;71. https://doi.org/10.1016/j.ejrad.2010.11.028\u003c/li\u003e\n\u003cli\u003eIzenman AJ. Modern Multivariate Statistical Techniques. Springer; 2008. https://link.springer.com/book/10.1007/978-0-387-78189-1\u003c/li\u003e\n\u003cli\u003eMacQueen J. Some methods for classification and analysis of multivariate observations. In: Proc 5th Berkeley Symp Math Stat Prob. 1967:281\u0026ndash;97. https://projecteuclid.org/journals/proceedings-of-the-fifth-berkeley-symposium-on-mathematical-statistics-and-probability/volume-1/issue-1/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992.full\u003c/li\u003e\n\u003cli\u003eLloyd SP. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129\u0026ndash;37. https://doi.org/10.1109/TIT.1982.1056489\u003c/li\u003e\n\u003cli\u003eDamstra J, Fourie Z, Huddleston Slater JJ, Ren Y. Accuracy of linear measurements from CBCT‑derived 3‑D surface models. Am J Orthod Dentofacial Orthop. 2010;137(4):S50\u0026ndash;S1. https://pubmed.ncbi.nlm.nih.gov/20122425/\u003c/li\u003e\n\u003cli\u003eGribel BF, Gribel MN, Manzi FR, Brooks SL, McNamara JA Jr. Accuracy and reliability of craniometric measurements on lateral cephalograms vs CBCT. Angle Orthod. 2011;81(1):26\u0026ndash;35. https://media.dent.umich.edu/labs/mcnamara/files/Accuracy%20and%20reliability%20of%20craniometric%20measurements%20on%20lateral%20cephalograms%20and%20CBCT.pdf\u003c/li\u003e\n\u003cli\u003eScarfe WC, Aboelsaad N, Farman AG, et al. CBCT in orthodontics. Aust Dent J. 2017;62(S1):33\u0026ndash;50. https://onlinelibrary.wiley.com/doi/abs/10.1111/adj.12479\u003c/li\u003e\n\u003cli\u003eSchwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769\u0026ndash;74. https://doi.org/10.1177/0022034520915714\u003c/li\u003e\n\u003cli\u003eArsiwala‑Scheppach LT, Kronig J, et al. Machine learning in dentistry: a scoping review. J Clin Med. 2023;12(3):937. https://pubmed.ncbi.nlm.nih.gov/36769585/\u003c/li\u003e\n\u003cli\u003eSchwendicke F, Chaurasia A, Arsiwala‑Scheppach L, et al. Deep learning for cephalometric landmark detection: systematic review and meta‑analysis. Clin Oral Investig. 2021;25:4299\u0026ndash;4312. https://link.springer.com/article/10.1007/s00784-021-03990-w\u003c/li\u003e\n\u003cli\u003ePauwels R, Smeets B, et al. Converting DAP to effective dose for dental CBCT. Physica Medica. 2023;110:102\u0026ndash;10. https://www.physicamedica.com/article/S1120-1797(23)00116-3/fulltext\u003c/li\u003e\n\u003cli\u003eLakhotia S, et al. Machine learning in dentistry: scoping review. PLOS Digit Health. 2025;4(9):e0000940. https://doi.org/10.1371/journal.pdig.0000940\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sana'a University","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":"CBCT, 3-D imaging, radiomics, explainable AI, principal component analysis, K‑means clustering, phenotyping, orthodontic diagnostics","lastPublishedDoi":"10.21203/rs.3.rs-8049618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8049618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Cone-beam computed tomography (CBCT) enables three-dimensional assessment of craniofacial structures; however, translating multiple inter‑correlated measurements into diagnostic insight remains challenging. Objective: To present an explainable engineering workflow combining principal component analysis (PCA) and K‑means clustering to identify skeletal Class II phenotypes in adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Sixty‑three CBCT variables from 120 Yemeni adults were standardized, reduced via PCA, and clustered (k = 2–8). Internal validity was evaluated with silhouette and Davies–Bouldin indices and stability across multiple random starts; phenotypes were mapped to clinical strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Seven components explained ≈60% of variance. A five‑cluster solution balanced cohesion, separation, and clinical interpretability, delineating deep‑bite and open‑bite tendencies, mandibular retrusion patterns, and incisor‑protrusive phenotypes. Solutions were stable across seeds and simple demographic strata.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: The PCA→K‑means pipeline provides a transparent, reproducible framework for AI‑assisted orthodontic diagnosis and phenotype‑based treatment planning, aligning with biomedical imaging and radiomics workflows.\u003c/p\u003e","manuscriptTitle":"CBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI Workflow","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:21:52","doi":"10.21203/rs.3.rs-8049618/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":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57566646,"name":"Dentistry"}],"tags":[],"updatedAt":"2026-01-03T18:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 17:21:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8049618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8049618","identity":"rs-8049618","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}