Artificial Intelligence in CBCT-Based Periodontal and Peri-Implant Diagnosis: A Scoping Review
DOI:
https://doi.org/10.15517/e3t19w34Keywords:
Artificial intelligence; Cone-beam computed tomography; Periodontitis; Peri-implantitis; Deep learning; Periodontal bone loss.Abstract
This scoping review aimed to map the extent, nature, and characteristics of the available evidence on artificial intelligence (AI) applied to cone-beam computed tomography (CBCT)-based periodontal and peri-implant diagnosis. A scoping review was conducted in accordance with Joanna Briggs Institute methodological guidance and reported following PRISMA-ScR. A systematic search of PubMed/MEDLINE, Scopus, Web of Science, and LILACS/BVS was performed up to April 3, 2026, without language restrictions. Records were exported in RIS format, managed in Zotero for duplicate removal, and screened in Rayyan using predefined eligibility criteria. Original studies applying AI-based methods to CBCT images for periodontal and/or peri-implant diagnostic purposes were included. Data were charted descriptively to summarize study characteristics, diagnostic targets, AI models, performance metrics, and methodological limitations. Of the 358 records identified, 174 were screened after removal of duplicate records and conference papers. Twelve reports underwent full-text assessment, and 7 studies were included in the final qualitative synthesis. Most studies were published between 2024 and 2026 and focused predominantly on periodontal applications, whereas only one addressed peri-implant marginal bone loss. The most consistent findings were observed in narrowly defined tasks, particularly furcation involvement detection, periodontal bone topography segmentation, periodontal defect mapping, and peri-implant marginal bone loss grading. Broader diagnostic platforms showed more variable performance. AI applied to CBCT-based periodontal and peri-implant diagnosis represents an emerging field with still limited methodological development. This review maps the CBCT-specific evidence base and identifies priorities for advancing toward more interpretable, externally validated, and clinically useful AI systems.
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