Digestive endoscopy is an important approach for the diagnosis and treatment of digestive tract diseases. With the expansion of gastrointestinal tumor screening projects, more and more patients and asymptomatic healthy people will receive digestive endoscopy, and digestive endoscopy training has become particularly important. The traditional patient-based training mode will be replaced by more standardized training mode. Due to the objectivity, safety and economic advantages, virtual reality (VR) simulation will be helpful for promoting and perfecting the standardized training mode of digestive endoscopists in China. This article reviewed the application, research progress, advantages, current limitations, and potential prospects of VR technique in the digestive endoscopy training.

Background: Artificial intelligence (AI) is a research hotspot in various fields of medicine at present. Its powerful image recognition and processing ability make it having a strong advantage in the field of digestive endoscopy. Aims: To construct a gastroscopy image recognition system based on AI, and to explore its value in the diagnosis of chronic atrophic gastritis (CAG). Methods: A total of 3 813 gastroscopy images were collected from patients who underwent gastroscopy and biopsy for pathological examination from April 2018 to August 2020 at Qingdao Municipal Hospital, including 1 927 images of CAG and 1 886 images of chronic non-atrophic gastritis (CNAG). Among them, 3 055 images were selected as training set (CAG/CNAG, 1 541/1 514) and 379 images (CAG/CNAG, 193/186) as the adjustment set, the remaining images as the test set. Deeping learning model was trained and verified. The receiver operating characteristic curve (ROC curve) and P-R curve were calculated. The sensitivity, specificity and accuracy of deep learning model and that of 3 less experienced endoscopists, 3 experienced endoscopists for diagnosis of CAG were compared. Results: The area under ROC curve of deep learning model for CAG was 0.916 8, the area under P-R curve was 0.931 6, and sensitivity was 89.1%, specificity was 74.2%, accuracy was 81.8%. The sensitivity, specificity and accuracy of deep learning model were superior to the three less experienced endoscopists, and even superior to some of the experienced endoscopists. Conclusions: The CAG diagnostic model based on deep learning technology has high sensitivity, specificity and accuracy, and can effectively identify CAG and assist the clinical endoscopists to diagnose CAG in gastroscopy.