Determination of gastric atrophy with artificial intelligence compared to the assessments of the modified Kyoto and OLGA classifications

Background and Aim: Gastric atrophy is a precancerous lesion. We aimed to clarify whether gastric atrophy determined by artificial intelligence (AI) correlates with the diagnosis made by expert endoscopists using several endoscopic classifications, the Operative Link on Gastritis Assessment (OLGA) classification based on histological findings, and genotypes associated with gastric atrophy and cancer. Methods: Two hundred seventy Helicobacter pylori-positive outpatients were enrolled. All patients' endoscopy data were retrospectively evaluated based on the Kimura-Takemoto, modified Kyoto, and OLGA classifications. The AI-trained neural network generated a continuous number between 0 and 1 for gastric atrophy. Nucleotide variance of some candidate genes was confirmed or selectively assessed for a variety of genotypes, including the COX-21195, IL-1 beta 511, and mPGES-1 genotypes. Results: There were significant correlations between determinations of gastric atrophy by AI and by expert endoscopists using not only the Kimura-Takemoto classification (P < 0.001), but also the modified Kyoto classification (P = 0.046 and P < 0.001 for the two criteria). Moreover, there was a significant correlation with the OLGA classification (P = 0.009). Nucleotide variance of the COX-2, IL-1 beta, and mPGES-1genes was not significantly associated with gastric atrophy determined by AI. The area under the curve values of the combinations of AI and the modified Kyoto classification (0.746) and AI and the OLGA classification (0.675) were higher than in AI alone (0.665). Conclusion: Combinations of AI and the modified Kyoto classification or of AI and the OLGA classification could be useful tools for evaluating gastric atrophy in patients with H. pylori infection as the risk of gastric cancer.