Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting

Aim: To comprehensively review digital technologies (including artificial intelligence, AI) for periodontal screening, diagnosis and prognosis in the dental setting, focusing on accuracy metrics. Materials and Methods: Two separate literature searches were conducted for periodontal screening and diagnosis (part I, scoping review) and prognosis (part II, systematic approach). PubMed, Scopus and Embase databases were searched. Results: In part I, 40 studies evaluated AI and advanced imaging on different substrata. The combination of AI with 2D radiographs was the most frequently investigated and demonstrated a high level of periodontitis detection and stage definition. In part II, eight studies, identified as having a high risk of bias, tested supervised machine learning models using 6–74 predictors. The models demonstrated variable predictive accuracy, often outperforming traditional risk assessment tools and classical statistical models in the few studies evaluating such comparisons. Conclusions: AI and advanced imaging techniques are promising for periodontal screening, diagnosis and prognosis in the dental setting, although the evidence remains inconsistent and inconclusive. In addition, AI-driven analysis of 2D radiographs (for diagnosis and staging of periodontitis), neural networks and the aggregation of multiple algorithms (for predicting tooth-related outcomes) appear to be the most promising approaches entering clinical application.