Diagnostic Efficiency of Artificial Intelligence Integrated Intraoral Mobile Photographs in Identification of Oral Potentially Malignant Disorders: An Umbrella Review.

Abstract

Background: Oral potentially malignant disorders (OPMDs) are precursors to oral squamous cell carcinoma (OSCC), a malignancy with high morbidity and mortality due to late diagnosis. Conventional diagnostic methods, though accurate, are invasive and often inaccessible in resource-limited areas. Intraoral mobile photographs integrated with artificial intelligence (AI) as a screening tool may offer a noninvasive, cost-effective, and accessible alternative for early detection of OPMDs. The study aimed to evaluate the diagnostic accuracy of mobile photographic models in identifying OPMDs.

Settings and design: An umbrella review synthesizing evidence from previously published systematic reviews/meta-analyses was carried out following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Methods and material: Databases including PubMed, Scopus, Embase, Web of Science, and through supplementary search engines like Google Scholar were searched. Inclusion criteria encompassed studies analyzing the diagnostic performance of mobile photographic models for OPMDs. Sensitivity, specificity, and applicability were examined.

Results: The pooled sensitivity was 90% (95% CI: 88.8%-92.6%) and specificity was 89% (95% CI: 86%-92%). Minimal heterogeneity in sensitivity (I ² = 34.01%) and higher variability in specificity (I ² = 93.34%) were observed. Funnel plots confirmed no publication bias.

Conclusions: Intraoral mobile photographs were effective in noninvasive OPMD detection, particularly in low-resource settings. Further research should aim to refine algorithms, standardize imaging, and validate performance in real-world scenarios.