Machine learning analysis of root canal anatomy in different ethnic populations using CBCT

Abstract

Root canal anatomy exhibits considerable variation among individuals and ethnic populations, presenting significant challenges for accurate diagnosis and successful endodontic treatment. Cone-beam computed tomography (CBCT) has transformed the evaluation of root canal morphology by providing high-resolution three-dimensional images that overcome many limitations of conventional radiography. The integration of machine learning (ML) with CBCT imaging has further enhanced the ability to automatically identify, segment, classify, and predict complex root canal configurations with improved speed, consistency, and diagnostic accuracy. ML algorithms, particularly deep learning models, can analyze large CBCT datasets to detect subtle anatomical variations and support personalized treatment planning across diverse populations. Understanding ethnicity-related differences in root canal morphology enables the development of more robust and generalizable predictive models while reducing the risk of missed canals and procedural complications. Despite promising advances, challenges remain regarding dataset diversity, annotation quality, algorithm bias, and clinical validation. Continued research involving multi-ethnic datasets and standardized imaging protocols is expected to improve the reliability and clinical applicability of AI-assisted endodontic diagnosis. Overall, the combination of machine learning and CBCT represents a significant advancement toward precision endodontics and individualized patient care.