Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.

BACKGROUND: Artificial intelligence (AI) has shown promise for diagnosing periodontal disease from dental radiographs. However, diagnostic performance across classification types (binary classification vs. staging classification) and imaging modalities remains unclear. This meta-analysis evaluates the accuracy of AI diagnostics for periodontitis, comparing binary and staging classifications across various imaging modalities. METHODS: A systematic meta-analysis reviewed AI-based periodontal diagnostic studies using periapical, panoramic, bitewing, or cone-beam computed tomographic radiographs. Random-effects models calculated pooled sensitivity, specificity, accuracy, F1-score, and area under the curve. Subgroup analyses were performed by imaging modality and heterogeneity (I²). RESULTS: In binary classification, periapical imaging showed a sensitivity of 87.2% and a specificity of 81.5%, while panoramic radiographs had an accuracy of 88.2%. In staging classification, panoramic images achieved the highest accuracy (88.9%) and specificity (85.4%), whereas periapical images showed higher sensitivity (76.4%). Diagnostic accuracy varied significantly across imaging modalities, contributing to heterogeneity among studies. CONCLUSIONS: This first meta-analysis comparing binary and staging AI classification emphasizes modality-specific approaches: panoramic imaging is suitable for screening and staging, whereas periapical radiographs support early detection, providing essential insights for clinical AI integration.