Artificial Intelligence Models for Diagnosis of Periodontitis Using Non-Invasive Biological Markers: A Systematic Review and Meta-Analysis of Patient-Based Studies

Abstract

Background/Objectives: Early diagnosis of periodontitis remains challenging using traditional clinical methods. This systematic review and meta-analysis evaluated the diagnostic accuracy of artificial intelligence (AI) models trained on non-invasive or minimally invasive biomarkers—including saliva, gingival crevicular fluid (GCF), and immunologic profiles—for diagnosing and classifying periodontitis in human subjects. Methods: A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, EMBASE, and Cochrane CENTRAL was conducted from database inception to June 2025. Eligible studies used AI or machine learning models with patient-derived biomarker data and reported diagnostic performance metrics. Results: Seven studies were included, employing various AI models such as random forest, artificial neural networks, and gradient boosting. Biomarkers were derived from saliva (n = 4), saliva-derived biomarkers from oral rinse (n = 1), immunologic profiles (n = 1), and tissue-based gene expression (n = 1). Reported area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.96. Meta-analysis of studies with comparable outcomes showed a pooled sensitivity of 0.89 (95% CI: 0.84–0.93), a specificity of 0.87 (95% CI: 0.80–0.92), and a summary AUC of 0.92. Subgroup analysis revealed that models using salivary biomarkers achieved a higher pooled AUC (0.94) than those using GCF or immunologic markers (AUC: 0.89). Sensitivity analyses excluding studies with unclear bias did not significantly alter pooled estimates, affirming robustness. The overall certainty of evidence was rated as moderate to high. Conclusions: AI-based diagnostic models utilizing salivary, microbiome, or immunologic biomarkers demonstrated quantitatively high accuracy; however, the overall certainty of evidence was rated as moderate to high due to limitations in study design and validation.