Artificial intelligence models for periodontitis classification: A systematic review.

Abstract

Objectives: The graded diagnosis of periodontitis has always been a difficulty for dentists. This systematic review aimed to investigate the performance of artificial intelligence (AI) models for periodontitis classification.

Data: This review includes original studies that explore the application of AI in periodontitis classification systems.

Sources: Two reviewers independently conducted a comprehensive search of literature published up to April 2024 in databases including PubMed, Web of Science, MEDLINE, Scopus, and Cochrane Library.

Study selection: A total of 28 articles were eventually included in this study, from which 10 mapping parameters were extracted and evaluated separately for each article.

Results: AI's diagnostic capabilities are comparable to those of a general dentist/periodontist, achieving an overall diagnostic accuracy rate of over 70% for periodontitis classification, with some reaching 80-90%. Variations in diagnosis accuracy rates were observed across different stages of periodontitis.

Conclusions: The AI model provides a novel and relatively reliable method for periodontitis classification. However, several key issues remain to be addressed, including access to and quality of data, interpretation of the decision-making process of the model, the ability of the model to generalize, and ethical and privacy considerations.

Clinical significance: The development of AI models for periodontitis classification is expected to assist dentists in improving diagnostic efficiency and enhancing diagnostic accuracy, and further development is expected to assist telemedicine and home self-testing.