Artificial intelligence for detection of periodontitis on radiographic images: a systematic review and diagnostic...

To determine the diagnostic accuracy of artificial intelligence (AI) models for the periodontitis on radiographic images, and to investigate the factors influencing diagnostic performance.

PubMed, Web of Science, and Cochrane Library were searched until April 2026. A bivariate random-effects model was used to synthesise sensitivity and specificity, and meta-regression explored heterogeneity.

Fourteen studies comprising 50,080 radiographic images were included. Seven studies performed diagnosis at the patient level and seven at the tooth or image level. At the patient level, the pooled sensitivity was 0.93 (95%CI, 0.86-0.97), pooled specificity was 0.88 (0.77-0.94), and the area under each curve (AUC) of summary receiver operating characteristic curves (SROC) was 0.96 (0.94-0.97). At the tooth level, pooled sensitivity was 0.90 (0.83-0.94), pooled specificity was 0.94 (95%CI, 0.91-0.96), and the AUC of SROC was 0.97 (0.96-0.98). The meta-regression analysis indicates that none of the algorithm architecture, imaging modality, or external validation are sources of heterogeneity.

AI models demonstrate excellent diagnostic accuracy for periodontitis detection. Well-designed prospective studies with robust clinical reference standards, external validation, and transparent reporting are urgently required before these tools can be responsibly integrated into routine dental care. The current evidence base underrepresents low- and middle-income countries, where the burden of periodontitis is greatest and AI-assisted screening could offer the greatest public health benefit.

AI-assisted radiographic screening may particularly benefit general dental practitioners and non-specialist clinicians in primary care or resource-limited settings, where access to periodontists is scarce and diagnostic variability is greatest. By flagging suspected moderate-to-severe periodontitis at the chairside within seconds, such tools could accelerate referral and triage decisions, enabling earlier definitive examination and treatment planning by specialists, while reducing the number of patients with advanced disease who are overlooked during routine dental visits.