Early detection of gastric neoplasia: is artificial intelligence the solution?

Abstract

Major efforts have focused on earlier endoscopic detection of gastric cancer, due to associated improved outcomes and reduced mortality. 1 Banks M Graham D Jansen M et al. British Society of Gastroenterology guidelines on the diagnosis and management of patients at risk of gastric adenocarcinoma. Gut. 2019; 68: 1545-1575 Crossref PubMed Scopus (130) Google Scholar Unfortunately, endoscopic detection remains inadequate, with potentially one in ten gastric cancers being missed. 2 Pimenta-Melo AR Monteiro-Soares M Libânio D Dinis-Ribeiro M Missing rate for gastric cancer during upper gastrointestinal endoscopy: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2016; 28: 1041-1049 Crossref PubMed Scopus (67) Google Scholar Furthermore, the current endoscopic standard of care, which uses white light imaging, has suboptimal sensitivity for early gastric cancer. 3 Yoshida N Doyama H Yano T et al. Early gastric cancer detection in high-risk patients: a multicentre randomised controlled trial on the effect of second-generation narrow band imaging. Gut. 2021; 70: 67-75 Crossref PubMed Scopus (17) Google Scholar Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trialThe use of an AI system during upper gastrointestinal endoscopy significantly reduced the gastric neoplasm miss rate. AI-assisted endoscopy has the potential to improve the yield of gastric neoplasms by endoscopists. Full-Text PDF