Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review

Abstract

Statement of problem: Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear.

Purpose: The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease.

Material and methods: A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus.

Results: Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6% to 99%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74% and 78.20%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7% to 73.72% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47% and 81%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4% and 99%.

Conclusions: AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.