Artificial Intelligence for Root Canal Segmentation on Radiographic Images: A Scoping Review

Objectives: The number, size, patency, and location of pulp canals are critical in endodontic treatment planning. This information is currently obtained through visual radiographic assessment, which is time-consuming and labor-intensive. Artificial intelligence (AI) could automate this task via accurate segmentation of root canals providing efficiency and consistency. This scoping review maps existing literature on the use of AI to automate root canal segmentation on radiographic images. Materials and Methods: We searched MEDLINE (Ovid), Embase, Scopus, and Web of Science for relevant studies up to January 8, 2025. Studies that used AI for root canal segmentation were included. Study selection was not limited by design, language, or date. Commentaries, retracted articles, and inaccessible full-text articles were excluded. Titles and abstracts were screened based on eligibility criteria, and the full-text of potentially relevant studies was assessed. Screening and data extraction were conducted in duplicate by independent reviewers, with disagreements resolved via consensus or a third-reviewer if necessary. Results: Out of 836 articles identified, 35 studies met the eligibility criteria and were retained for synthesis. Data extraction focused on the country of origin, study design, imaging modalities, obturation status, type of teeth analyzed, AI models used, and results. Modalities included were mostly cone beam computed tomography (CBCT, 51%), followed by panoramic (17%) and periapical (14%). AI-based models, particularly those employing CNNs, reported accuracies ranging from 0.73 to 0.99 and sensitivities from 0.72 to 1. These models were effective across all imaging modalities with most studies reporting improved diagnostic precision and reduced time compared with manual methods. Conclusions: AI-based root canal segmentation has clinical value by increasing accuracy in identifying root canal anatomy prior to treatment. This will preserve clinicians' time and reduce the risk of treatment failure. This review highlights current status of this technological application and identifies areas to refine these technologies for broad clinical application to enhance patient outcomes in endodontic care. Clinical Relevance: The application of AI in root canal segmentation offers significant clinical benefits by improving the accuracy and efficiency of identifying root canal anatomy. This can lead to better treatment planning, reduced procedure times, and lower risk of endodontic failure. As AI technology continues to evolve, its integration into endodontic practice has the potential to enhance patient outcomes and streamline clinical workflows.