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


Fatma P. H., Magat G., Karobari M. I., Buchanan G. D., Kopbayeva M., Taha N., ...Daha Fazla

International Endodontic Journal, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1111/iej.70121
  • Dergi Adı: International Endodontic Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MEDLINE
  • Anahtar Kelimeler: Bayesian hierarchical modelling, cone-beam computed tomography, mandibular first premolar, root canal morphology, Vertucci classification
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

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.