The diagnostic value of artificial intelligence in oral squamous cell carcinoma: A systematic review and meta-analysis

Abstract

Objective

To evaluate the diagnostic performance of artificial intelligence (AI) in detecting oral squamous cell carcinoma (OSCC) through a systematic review and meta-analysis.

Methods

A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and other databases for studies published from January 2000 to November 2023. Studies that evaluated AI for OSCC diagnosis with sufficient data to calculate diagnostic accuracy were included. The methodological quality was assessed using QUADAS-2. The primary outcomes were pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). A bivariate random-effects model was used for analysis.

Results

Twenty-four studies comprising 18,574 specimens were included. The pooled sensitivity was 0.95 (95 % CI: 0.90–0.98), and the pooled specificity was 0.95 (95 % CI: 0.91–0.98). The pooled PLR was 2.60 (95 % CI: 1.91–3.28), and the NLR was 0.10 (95 % CI: 0.07–0.17), with a DOR of 26.0 (95 % CI: 12.1–55.9). Significant heterogeneity was observed across studies (I² = 97.5 % for sensitivity and I² = 97.8 % for specificity). Deep learning algorithms demonstrated superior performance compared to conventional machine learning methods.

Conclusion

AI demonstrates high diagnostic accuracy for OSCC detection, suggesting its potential value as an adjunctive diagnostic tool in clinical practice. However, high heterogeneity among studies indicates the need for standardized methodologies and external validation before widespread implementation.