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

Area of Science:

• Endodontics
• Dental Anatomy
• Radiology

Background:

• Root canal morphology is critical for successful endodontic treatment.
• Mandibular first premolars (M1Ps) exhibit complex and variable root canal anatomy.
• Standardized classification systems like Vertucci's are essential but lack extensive global data.

Purpose of the Study:

• To evaluate the global distribution of Vertucci root canal morphology in M1Ps.
• To identify determinants influencing M1P canal configurations worldwide.
• To utilize Bayesian hierarchical modeling for robust analysis of anatomical variations.

Main Methods:

• Analysis of Cone-Beam Computed Tomography (CBCT) data from M1Ps across 21 countries.
• Application of the Vertucci classification system for canal morphology.
• Bayesian hierarchical multinomial logistic regression to model country-level variations and predictor effects.

Main Results:

• Vertucci Type I configuration was estimated at 73.4% globally.
• Non-Type I configurations (V, III, IV, II) and unclassified patterns occurred with notable frequencies (8.2%, 3.7%, 2.9%, 1.3%, and 9.9% respectively).
• Significant inter-country variability was observed for non-Type I and unclassified configurations, while Type I remained predominant.

Conclusions:

• Vertucci Type I is the most common M1P canal morphology globally, but significant variations exist.
• Clinically relevant non-Type I and unclassified canal patterns necessitate careful consideration in endodontic practice.
• Bayesian hierarchical modeling provides a robust framework for quantifying anatomical heterogeneity and supporting cross-country comparisons.