Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis

The aim of this study is to evaluate the performance of DL algorithms in diagnosing early gastric cancer (EGC) using white light endoscopic images.

A systematic literature search was conducted in PubMed, Embase, Cochrane Library, and Web of Science up to July 25, 2025. Sensitivity and specificity were pooled for internal and external validation sets. The comparison between DL algorithms and expert endoscopists was performed using paired forest plots. Meta-regression was used to identify sources of heterogeneity.

In the internal validation, 15 studies comprising 37,037 images (range: 433-9,650) were included. Pooled sensitivity and specificity were 0.91 (95% CI: 0.82-0.95) and 0.93 (95% CI: 0.87-0.97), respectively. Meta-regression showed that heterogeneity in sensitivity and specificity was significantly associated with training dataset size. For external validation, 4 studies with 3,579 images (range: 200-1,514) were included, yielding pooled sensitivity and specificity of 0.82 (95% CI: 0.61-0.93) and 0.83 (95% CI: 0.74-0.90), respectively. No significant difference was observed between deep learning models and expert endoscopists in diagnostic sensitivity and specificity.

Deep learning algorithms exhibit high diagnostic performance in detecting early gastric cancer using white-light endoscopy. The diagnostic accuracy of DL models is comparable to that of expert endoscopists, supporting their potential role as a clinical decision-support tool.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251112418, identifier CRD420251112418.