Journal of clinical periodontology · 2025
Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting.
СтатьяScoping ReviewПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Farina Roberto, Simonelli Anna, Trombelli Leonardo, Ettmayer Johanna B, Schmid Jan L, Ramseier Christoph A
Аннотация
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.
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.