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Bayesian Hierarchical Modelling of Root Canal Morphology in Mandibular First Premolars Across 21 Countries

Fatma Pertek HatipoğluDepartment of Endodontics Recep Tayyip Erdoğan University Rize TurkeyGüldane MağatDepartment of Oral Radiology, Faculty of Dentistry Necmettin Erbakan University Konya TurkeyMohmed Isaqali KarobariDepartment of Conservative Dentistry and Endodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu IndiaGlynn Dale BuchananUniversity of PretoriaMaira KopbayevaSchool of Dentistry, Department of Therapeutics Dentistry Kazakh National Medical University Named After S.D. Asfendiyarov Almaty KazakhstanNessrin TahaDepartment of Conservative Dentistry Jordan University of Science and Technology Irbid JordanNisrein MakahlehDental Teaching Clinics Jordan University of Science and Technology Irbid JordanRafael Fernández-GrisalesDepartment of Endodontics, School of Dentistry CES University Medellín ColombiaOlga Ye. BekjanovaFaculty of Dentistry Tashkent State Medical University Tashkent UzbekistanPeter LuuFaculty of Medicine and Health, Sydney Dental School The University of Sydney Camperdown New South Wales AustraliaSebastian BürkleinCentral Interdisciplinary Ambulance in the School of Dentistry University of Münster Münster GermanyAbdulbaset A. MufadhalDepartment of Restorative and Aesthetic Dentistry, Faculty of Dentistry Sana'a University Sana'a YemenXenos PetridisDepartment of Endodontics, Section of Dental Pathology and Therapeutics, School of Dentistry National and Kapodistrian University of Athens Athens GreeceMaría Fernanda MoraEndodontic Department of Dentistry Universidad Central del Ecuador Quito EcuadorSurendar SugumaranDepartment of Cardiology and Comprehensive Care Dentistry NYU College of Dentistry New York New York USASafaa AllawiDepartment of Endodontic and Operative Dentistry, Faculty of Dentistry Damascus University Damascus SyriaAnja IvicaUniversity of Zagreb School of Dental Medicine Zagreb CroatiaWen Yi LimDepartment of Restorative Dentistry National Dental Centre Singapore SingaporeAbdulrahman FadagEndodontics Department, Faculty of Dentistry Ibb University Ibb YemenRohan JagtapDivision of Oral and Maxillofacial Radiology University of Mississippi Medical Center School of Dentistry Jackson Mississippi USAJosé Martín-CrucesEndodontics and Restorative Dentistry Unit, School of Medicine and Dentistry Universidade de Santiago de Compostela Santiago de Compostela SpainTomasz KulczykDepartment of Diagnostics Poznan University of Medical Sciences Poznan PolandSuha AlfirjaniDepartment of Conservative Dentistry and Endodontics University of Benghazi Benghazi LibyaPaulo PalmaCenter for Innovation and Research in Oral Sciences (CIROS) and Institute of Endodontics, Faculty of Medicine University of Coimbra Coimbra PortugalÖmer HatipoğluDepartment of Restorative Dentistry Recep Tayyip Erdogan University Rize Turkey
ABI

Аннотация

BACKGROUND: Understanding root canal morphology is crucial for successful endodontic treatment; however, the anatomy of mandibular first premolars (M1Ps) remains one of the most variable and challenging aspects. The Vertucci classification provides a standardised framework for describing canal configurations; however, population-level data integrating multiple countries are scarce. This study aimed to evaluate the global distribution and determinants of Vertucci canal morphology in M1Ps using a Bayesian hierarchical model. METHODS: Cone-beam computed tomography (CBCT) data of M1Ps from 21 countries were analysed. The Vertucci classification was used as the categorical outcome variable. The predictors included tooth side (34/44), voxel size, field of view (FOV), sex and age, with the country modelled as a random intercept. A Bayesian hierarchical multinomial logistic regression was fitted using the brms package (rstan backend) with weakly informative priors. Posterior estimates were expressed as odds ratios (OR) and 95% credible intervals (CrI), and model-based predicted probabilities were computed for each Vertucci type. RESULTS: Bayesian modelling estimated the posterior probability of Vertucci Type I configuration at 73.4% (95% CrI: 63.8%-81.5%). Non-Type I configurations showed lower but credible probabilities, including Type V (8.2%, 3.6%-15.9%), Type III (3.7%, 1.6%-7.7%), Type IV (2.9%, 1.2%-6.3%) and Type II (1.3%, 0.5%-3.1%). Unclassified canal patterns accounted for approximately one-tenth of the MnP1s (9.9%, 3.9%-19.2%). Substantial variability was observed between countries for non-Type I and unclassified configurations, whereas Type I remained consistently predominant. Sex and age exerted modest effects, whereas tooth side and field of view showed no meaningful associations. Increasing the voxel size was associated with a slight reduction in the probability of Type I and marginal increases in Type V and unclassified configurations. CONCLUSIONS: Although Vertucci Type I configuration predominates globally in MnP1s, clinically relevant non-Type I and unclassified canal patterns occur with non-negligible frequency and vary across populations. Bayesian hierarchical modelling enables the robust quantification of anatomical heterogeneity and uncertainty, supporting more reliable cross-country comparisons and cautious interpretation of less common canal configurations.

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