Artificial Intelligence for Detection of Root Canal Fillings and Evaluation of Obturation Quality on Dental Radiographs: A Systematic Review.

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Abstract

To systematically evaluate evidence on artificial intelligence (AI) for root canal filling (RCF) detection and radiographic assessment of obturation quality. Eligible studies assessed AI-based models for RCF detection and/or obturation quality assessment using dental radiographs and cone-beam computed tomography (CBCT). PubMed, Embase, Scopus, ClinicalTrials.gov, Cochrane Library, Web of Science, Google Scholar, and Open Science Framework were searched up to March 2026. Studies evaluating AI-based models for RCF detection and/or obturation quality assessment were included. Data were synthesized qualitatively. Risk of bias was assessed with QUADAS-2 and certainty of evidence with GRADE. Thirteen studies were included, comprising 12,513 examinations from periapical, panoramic, bitewing, and CBCT images. For RCF detection, sensitivity ranged from 0.85 to 1.00 and specificity from 0.92 to 1.00. For obturation quality assessment, sensitivity ranged from 0.79 to 1.00 and specificity from 0.99 to 1.00, with better performance in periapical radiographs and CBCT than panoramic radiographs. Risk of bias was mainly related to patient selection and lack of external validation, and substantial heterogeneity was observed. AI-based models show promise for RCF detection, whereas performance for obturation quality assessment is more variable and modality-dependent. AI may serve as an adjunct in endodontic radiographic evaluation. AI-based systems may affect clinical workflows through automated radiographic pre-screening, image interpretation support, and real-time second-opinion validation. These tools may reduce interpretation time, improve diagnostic consistency, and support detection of endodontic findings, particularly in high-volume settings. External validation and prospective studies are required before routine clinical implementation.

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