Pertek Hatipoğlu, FatmaMagat, GüldaneKarobari, Mohmed IsaqaliBuchanan, Glynn DaleKopbayeva, MairaTaha, NessrinMakahleh, NisreinFernández-Grisales, RafaelBekjanova, OlgaLuu, PeterBürklein, SebastianMufadhal, AbdulbasePetridis, XenosMora, María FernandaSugumaran, SurendarAllawi, SafaaIvica, AnjaLim, Wen YiFadag, AbdulrahmanJagtap, RohanMartín-Cruces, JoséKulczyk, TomaszAlfirjani, SuhaPalma, Paulo J.Hatipoğlu, Ömer2026-03-252026Pertek Hatipoğlu, F., G.Magat, M. I.Karobari, et al. 2026. “Bayesian Hierarchical Modelling of Root Canal Morphology in Mandibular First Premolars Across 21 Countries.” International Endodontic Journal 1–14. https://doi.org/10.1111/iej.70121.0143-2885 (print)1365-2591 (online)10.1111/iej.70121http://hdl.handle.net/2263/109305DATA AVAILABILITY STATEMENT : The data that support the findings of this study are available from the corresponding author upon reasonable request.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.en© 2026 British Endodontic Society. Published by John Wiley & Sons Ltd. This is the pre-peer reviewed version of the following article : “Bayesian Hierarchical Modelling of Root Canal Morphology in Mandibular First Premolars Across 21 Countries.” International Endodontic Journal 1–14. https://doi.org/10.1111/iej.70121. The definite version is available at : https://onlinelibrary.wiley.com/journal/13652591.Bayesian hierarchical modellingVertucci classificationRoot canal morphologyCone-beam computed tomographyMandibular first premolarPostprint Article