Systematic review and meta-analysis. Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection

Background: The endoscopic diagnosis of Helicobacter-pylori ( H.pylori ) infection and gastric precancerous lesions(GPL), namely atrophic-gastritis and intestinal-metaplasia, still remains challenging. Artificial intel- ligence(AI) may represent a powerful resource for the endoscopic recognition of these conditions. Aims: To explore the diagnostic performance(DP) of AI in the diagnosis of GPL and H.pylori infection. Methods: A systematic-review was performed by two independent authors up to September 2021. Inclu- sion criteria were studies focusing on the DP of AI-system in the diagnosis of GPL and H.pylori infection. The pooled accuracy of studies included was reported. Results: Overall, 128 studies were found (PubMed-Embase- Cochrane Library) and four and nine studies were finally included regarding GPL and H.pylori infection, respectively. The pooled- accuracy(random effects model) was 90.3%(95%CI 84.3–94.9) and 79.6%(95%CI 66.7–90.0) with a signifi- cant heterogeneity[I 2 = 90.4%(95%CI 78.5–95.7);I 2 = 97.9%(97.2–98.6)] for GPL and H.pylori infection, respec- tively. The Begg’s-test showed a significant publication-bias( p = 0.0371) only among studies regarding H.pylori infection. The pooled-accuracy(random-effects-model) was similar considering only studies using CNN-model for the diagnosis of H.pylori infection: 74.1%[(95%CI 51.6–91.3);I 2 = 98.9%(95%CI 98.5–99.3)], Begg’s-test( p = 0.1416) did not show publication-bias. Conclusion: AI-system seems to be a good resource for an easier diagnosis of GPL and H.pylori infection, showing a pooled-diagnostic-accuracy of 90% and 80%, respectively. However, considering the high het- erogeneity, these promising data need an external validation by randomized control trials and prospective real-time studies.