Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.

Objective: Timely diagnosis of early gastric cancer (EGC) is significantly associated with patient prognosis, but traditional endoscopic diagnosis relies on the physician's experience and has certain limitations. This study comprehensively evaluated the accuracy of artificial intelligence (AI) in the diagnosis of EGC through meta-analysis and compared the performance ability of different AI models. Methods: PubMed, Embase, Web of Science Cochrane Library, and China National Knowledge Infrastructure databases were systematically searched (established until January 2025), and studies evaluating the accuracy of AI models in the diagnosis of EGC were included, requiring reporting of sensitivity and specificity, or providing data for calculating these indicators. Data were extracted independently by two reviewers, and sensitivity and specificity were pooled using a bivariate random effects model, and subgroup analysis was performed by AI model type. The primary outcome measures were the summary sensitivity, specificity, and area under the curve (AUC) of all AI models. Results: Of 26 studies involving 43,088 patients were included. Meta-analysis results showed that the summary sensitivity of the AI model was 0.90 (95%CI: 0.87-0.93), the specificity was 0.92 (95%CI: 0.87-0.95), and the AUC was 0.96 (95%CI: 0.94-0.98), respectively. Subgroup analysis showed that the sensitivity of deep convolutional neural network (DCNN) was higher than that of traditional CNN (0.94 vs 0.89), while the specificity was almost equivalent (0.91 vs 0.91). In dynamic video verification, the AUC of the AI model reached 0.98, which was significantly better than the clinician level (AUC 0.85-0.90). Conclusion: The AI model, especially the DCNN architecture, showed excellent accuracy in the diagnosis of EGC. Future research should focus on the dynamic effect of the model, improvement of interpretability, and multicenter prospective validation. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251003071, identifier CRD420251003071.