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Evaluation of root and canal morphology of permanent maxillary molars: A CBCT study with a new classification
1Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia.
Background:
A thorough understanding of root canal anatomy is essential for achieving predictable endodontic outcomes, particularly in maxillary molars, which are anatomically complex. The presence of additional canals, especially in the mesiobuccal root, remains a frequent cause of missed anatomy and treatment failure. Despite the growing use of cone-beam computed tomography (CBCT), population-specific data using contemporary classification systems remain limited.
Aim:
The present study aimed to assess the root and canal configurations of permanent maxillary molars in a Saudi Arabian subpopulation using CBCT and the classification system of Ahmed et al. METHODOLOGY: This retrospective CBCT-based study evaluated 703 permanent maxillary molars from 206 patients in a Saudi Arabian subpopulation. Root and canal morphology were assessed using the Ahmed et al. classification system. The influence of tooth type, age, and gender on canal configuration was analysed using chi-square tests (P<0.05).
Result:
Three-rooted morphology was predominant, with the most common configuration being 3TN MB1 DB1 P1 (38.83%). Configurations involving a second mesiobuccal canal (MB2 and MB2-1) collectively accounted for more than 50% of all maxillary molars examined. Canal configuration was significantly associated with tooth type, age, and gender (P<0.001). Greater anatomical complexity was observed in maxillary first molars, younger individuals, and male patients.
Conclusion:
The predominant morphology of maxillary molars in this Saudi subpopulation was a three-rooted configuration, with considerable variation in the mesiobuccal canal system. Additional canals, particularly MB2 configurations, were frequently observed and should be carefully assessed during endodontic treatment. Tooth type, age, and gender were significantly associated with canal morphology. The Ahmed et al. classification system provided a comprehensive framework for describing these anatomical variations. Future studies should investigate the relationship between canal morphology and treatment outcomes and explore AI-assisted CBCT analysis for automated detection of complex canal anatomy.
