Precision and Accessibility in Periodontal Diagnosis: A Comparative Study of AI and Clinical Expertise

Aim: This study aimed to validate and compare the diagnostic accuracy of a developed AI model against a periodontist, using an expert periodontist as the gold standard. The goal was to evaluate the AI’s potential as a scalable triage tool for periodontal diagnosis in clinical settings. Materials and Methods: The AI model, developed from 2,000 panoramic radiographs, was clinically implemented using 300 anonymized images from the SIDEXIS program. It quantified alveolar bone loss to stage periodontitis, with diagnostic performance compared to a periodontist using expert evaluation as the reference. Results: The expert periodontist classified the cases as follows: 3.3% non-periodontitis, 10.3% stage I, 47.0% Stage II, 38.7% stage III, and 0.7% stage IV. The AI model demonstrated a higher overall consistency rate with the expert (71.7%) compared to the periodontist (62.7%). This superior performance was particularly notable in stage II (AI: 40% vs. periodontist: 28%) and stage III (AI: 29% vs. periodontist: 27%) classifications. The level of agreement with the expert, measured by Cohen’s κ, was moderate and similar for both the periodontist (κ = 0.530) and the AI model (κ = 0.497). The AI model exhibited significantly higher accuracy (97% vs. 87.3%) and perfect sensitivity (100% vs. 88.3%) but considerably lower specificity (10% vs. 60%) compared to the periodontist. Conclusions: The findings indicate AI’s strong potential as a scalable triage tool to democratize periodontal care, delivering high diagnostic accuracy. With further refinement to improve specificity, such models could ensure equitable access to early intervention, overcoming geographic and socioeconomic barriers.