Background/Objectives: Root canal treatment (RCT) is a common dental procedure performed to preserve teeth by removing infected or at-risk pulp tissue caused by caries, trauma, or other pulpal conditions. A successful outcome, among others, depends on accurate identification of the root canal anatomy, planning a suitable therapeutic strategy, and ensuring a bacteria-tight root canal filling. Despite advances in dental techniques, there remains limited integration of computational methods to support key stages of treatment. This review aims to provide a comprehensive overview of computational methods applied throughout the full workflow of RCT, examining their potential to support clinical decision-making, improve treatment planning and outcome assessment, and help bridge the interdisciplinary gap between dentistry and computational research. Methods: A comprehensive literature review was conducted to identify and analyze computational methods applied to different stages of RCT, including root canal segmentation, morphological analysis, treatment planning, quality evaluation, follow-up, and prognosis prediction. In addition, a taxonomy based on application was developed to categorize these methods based on their function within the treatment process. Insights from the authors’ own research experience were also incorporated to highlight implementation challenges and practical considerations. Results: The review identified a wide range of computational methods aimed at enhancing the consistency and efficiency of RCT. Key findings include the use of advanced image processing for segmentation, image analysis for diagnosis and treatment planning, machine learning for morphological classification, and predictive modeling for outcome estimation. While some methods demonstrate high sensitivity and specificity in diagnostic and planning tasks, many remain in experimental stages and lack clinical integration. There is also a noticeable absence of advanced computational techniques for micro-computed tomography and morphological analysis. Conclusions: Computational methods offer significant potential to improve decision-making and outcomes in RCT. However, greater focus on clinical translation and development of cross-modality methodology is needed. The proposed taxonomy provides a structured framework for organizing existing methods and identifying future research directions tailored to specific phases of treatment. This review serves as a resource for both dental professionals, computer scientists and researchers seeking to bridge the gap between clinical practice and computational innovation.
Objective(s) Dental micro-CT (µCT) provides highly detailed visualization of root canal
structures and treatment defects. However, the relationship between root canal morphologies
and the outcomes of root canal treatment (RCT) remains insufficiently understood. Therefore,
we introduce a data analysis framework that standardizes the representation of root canal
anatomy and associated defects. Our aim is to investigate how morphological features of root
canals influence RCT outcomes, potentially improving future clinical treatment options.
Method(s) An integrated framework is being developed to segment root canal structures from
high-resolution dental µCT datasets using deep learning and represent them in a compact,
standardized, and comprehensive manner. The framework enables continuous visualization
along the full length of the root canal, allowing spatial mapping of defects such as cracks and
pores. Interactive exploration is supported through a linking-and-brushing paradigm,
facilitating analysis of relationships between root canal morphologies and defect occurrences.
Our framework reduces dimensionality of complex 3D data while preserving key morphological
features, thus enabling comparisons and defect analysis across µCT samples.
Result(s) The framework produces a simplified yet information-rich representation of complex
volumetric µCT data. Mapping root canal structures along their length reveals spatial patterns
of defect occurrences that are difficult to quantify with conventional visualization methods.
Interactive exploration further enhances identification of relationships between anatomical
configurations and defect distribution, offering new insights into potential causes of treatment
failures.
Conclusion(s) This work presents a concept and a prototype data platform for analysing root
canal structures and treatment defects at the microscale. The method contributes to a better
understanding of complex µCT data by supporting the identification of structural factors
influencing treatment outcomes, which may ultimately drive the development of improved
clinical procedures, instruments, and materials in endodontics.
