Predictive Model of Endodontic Access in Maxillary Central Incisors Using Cone Beam Computed Tomography

Abstract

Root canal access is essential for successful root canal treatment, yet it poses significant risks in teeth with calcified or constricted canals, such as root perforation or excessive loss of healthy dentin. The aim of this study was to develop a predictive model that could guide the design of a conservative, accurate endodontic access in maxillary central incisors using cone beam computed tomography (CBCT). In this retrospective cross-sectional study, CBCT scans from 135 maxillary central incisors were analyzed to obtain anatomical and demographic data. Twenty-four variables significantly correlated with three key aspects of access design—access starting point, depth to the pulp horn, and access angle (target variables). Mathematical functions were formulated using non-linear regression, and the resultant model was validated for the three target variables with a new set of 18 maxillary central incisors (R² > 0.68, W > 0.90). The results showed that age, tooth length, and specific CBCT-derived parameters, such as starting point, angle, and depth, which are related to the tooth's access opening, strongly influenced the predicted access cavity parameters. This predictive model has the potential to be integrated into dynamic navigation software, optimizing endodontic access and reducing iatrogenic errors for practitioners.