Diagnostic Performance of Artificial Intelligence Models for Periodontitis Disease Detection Using Panoramic...

Background/Objectives: Periodontitis is a highly prevalent inflammatory disease and a major cause of tooth loss worldwide. Accurate diagnosis requires integration of clinical and radiographic findings, but interpretation of panoramic radiographs is subject to variability. Artificial intelligence (AI) has emerged as a promising adjunct for radiographic assessment. This systematic review evaluated the diagnostic performance of AI-based models for detecting periodontitis using panoramic radiographic images. Methods: A systematic search of PubMed, Scopus, and Web of Science identified studies published between 1 January 2015 and 1 March 2026. Eligible studies assessed AI models for periodontitis detection on panoramic radiographs and used either clinically confirmed periodontal diagnosis or expert radiographic annotation as the reference standard. Data extraction and quality assessment were performed independently by two reviewers using the QUADAS-2 tool. Owing to heterogeneity in AI architectures, datasets, and outcome measures, a narrative synthesis was conducted. Results: Nine studies met the inclusion criteria, comprising more than 20,000 radiographs. AI models included convolutional neural networks (CNNs), segmentation-based systems, and hybrid architectures. Sensitivity ranged from 0.795 to 1.00, specificity from 0.784 to 0.99, and AUC values from 0.843 to 0.967. Studies using clinical periodontal diagnosis as the reference standard generally reported lower performance than those relying solely on expert annotation. Only four studies performed external validation, and dataset sizes varied widely. One study combining panoramic and periapical radiographs showed moderate diagnostic performance. Conclusions: AI-based diagnostic models demonstrate promising performance for detecting periodontitis on panoramic radiographs, with several studies reporting high sensitivity and AUC values. However, heterogeneity in reference standards, limited external validation, and inconsistent dataset quality restrict generalizability. AI should be considered an adjunct to, rather than a replacement for, comprehensive clinical periodontal examination. Standardized datasets and robust external validation are needed to support clinical implementation.