Objectives: Detecting early gastric cancer is difficult, and it may even be overlooked by experienced endoscopists. Recently, artificial intelligence based on deep learning through convolutional neural networks (CNNs) has enabled significant advancements in the field of gastroenterology. However, it remains unclear whether a CNN can outperform endoscopists. In this study, we evaluated whether the performance of a CNN in detecting early gastric cancer is better than that of endoscopists.Methods: The CNN was constructed using 13,584 endoscopic images from 2639 lesions of gastric cancer. Subsequently, its diagnostic ability was compared to that of 67 endoscopists using an independent test dataset (2940 images from 140 cases).Results: The average diagnostic time for analyzing 2940 test endoscopic images by the CNN and endoscopists were 45.5 ± 1.8 s and 173.0 ± 66.0 min, respectively. The sensitivity, specificity, and positive and negative predictive values for the CNN were 58.4%, 87.3%, 26.0%, and 96.5%, respectively. These values for the 67 endoscopists were 31.9%, 97.2%, 46.2%, and 94.9%, respectively. The CNN had a significantly higher sensitivity than the endoscopists (by 26.5%; 95% confidence interval, 14.9–32.5%).Conclusion: The CNN detected more early gastric cancer cases in a shorter time than the endoscopists. The CNN needs further training to achieve higher diagnostic accuracy. However, a diagnostic support tool for gastric cancer using a CNN will be realized in the near future.

The role of Toll-like receptor (TLR) signaling has attracted much attention in the development of hepatic inflammation and hepatocellular carcinoma (HCC). We herein sought to determine the role of TLRs and responsible cells in steatohepatitis-related HCC. We used hepatocyte-specific Pten-deficient (Pten^Δhep) mice, which exhibit steatohepatitis followed by liver tumor formation, including HCC. We then generated Pten^Δhep/Tlr4^−/− and Pten^Δhep/Tlr2^−/− double-mutant mice and investigated the role of macrophages using reconstitution of bone marrow (BM)-derived cells, chemical depletion of macrophages, and isolated macrophages. Tlr4 but not Tlr2 deficiency in the Pten^Δhep mice suppressed tumor growth as well as hepatic inflammation. Gut sterilization by an antibiotic mixture reduced the portal LPS levels as well as tumor growth in the Pten^Δhep mice. Tumor growth was also decreased by reconstitution of BM-derived cells to Tlr4^−/− BM cells. In addition, chemical depletion of macrophages significantly reduced tumor size and numbers. Macrophages expressing Ly6C were increased in number, which was associated with inflammation and tumor progression in the Pten^Δhep mice. Hepatic macrophages isolated from the Pten^Δhep mice abundantly expressed the Ly6C gene and produced much more IL-6 and TNFα in response to LPS. These proinflammatory cytokines induced the proliferation of HCC cells as well as oval cells, putative cancer progenitor cells. Indeed, putative cancer progenitor cells emerged before the development of macroscopic liver tumors and then increased in number under sustained inflammation. TLR4 on macrophages contributes to the development of steatohepatitis-related HCC in mice.

Image recognition using artificial intelligence (AI) has progressed significantly due to innovative technologies such as machine learning and deep learning. In the field of gastric cancer (GC) management, research on AI‐based diagnosis such as anatomical classification of endoscopic images, diagnosis of Helicobacter pylori infection, and detection and qualitative diagnosis of GC is being conducted, and an accuracy equivalent to that of physicians has been reported. It is expected that AI will soon be introduced in the field of endoscopic diagnosis and management of gastric cancer as a supportive tool for physicians, thus improving the quality of medical care.