Stratification of gastric cancer risk using a deep neural network

Stratifying gastric cancer (GC) risk and endoscopy findings in high-risk individuals may provide effective surveillance for GC. We developed a computerized image- analysis system for endoscopic images to stratify the risk of GC.

The system was trained using images taken during endoscopic examinations with non-magnified white-light imaging. Patients were classified as high-risk (patients with GC), moderate-risk (patients with current or past Helicobacter pylori infection or gastric atrophy), or low-risk (patients with no history of H. pylori infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high-, moderate-, and low-risk groups, respectively.

Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and H. pylori serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low-, moderate-, and high risk, respectively. The prevalence of GC in the low-, moderate-, and high-risk groups was 2.2, 8.8, and 16.4%, respectively (P = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement).

The current AI system detected significant differences in the prevalence of GC among the low-, moderate-, and high-risk groups, suggesting its potential for stratifying GC risk.