Innovations using digital technologies for periodontal diagnosis and prognosis: a narrative review

Abstract

Objective This narrative review aims to identify and evaluate the available scientific literature on digital technologies assisting clinicians in periodontal diagnosis and prognosis, including electronic dental record systems, mobile applications, consumer-engaging platforms, and image recognition technologies. Methods A literature search was performed in PubMed/MEDLINE, supplemented by manual screening of reference lists. Literature management was supported by Covidence. Given the narrative nature of this review, studies were selected based on relevance to the review topics without application of a formal systematic screening protocol. Results The reviewed literature demonstrates considerable progress in periodontal care supported by artificial intelligence (AI). Deep learning models, particularly convolutional neural networks, have shown diagnostic accuracy rates ranging from approximately 70–98% for periodontitis classification from dental radiographs, with some models achieving very high sensitivity for bone loss detection. Mobile health applications and gamification strategies have shown promise for improving oral hygiene behaviors and patient engagement. Conclusions AI and digital technologies represent promising tools for periodontal care, offering the potential for enhanced diagnostic accuracy, streamlined clinical workflows, and improved patient engagement. However, significant challenges remain regarding standardization, validation in diverse populations, and integration into clinical practice. Future research should focus on conducting multicenter prospective trials, developing standardized reporting frameworks, and addressing algorithmic bias and data privacy concerns.