Linked color imaging combined with Kyoto Classification of Gastritis to establish a training model for general practitioners with a special interest in digestive diseases: a quantitative study of general practitioner training in China

Abstract

Abstract Background Linked color imaging (LCI) can improve the diagnostic rate of Helicobacter pylori (H. pylori) infection-related gastritis, and Kyoto Classification of Gastritis contributes to the diagnosis of H. pylori infection under endoscopy. The present study aims to combine LCI with Kyoto Classification of Gastritis to establish a training model for general practitioners (GPs) with a special interest in digestive diseases. Methods Gastroenterology sub-specialty training was conducted for GPs. After analysis by expert endoscopists, a simplified Kyoto Classification of Gastritis was generated to prepare the questionnaire. The questionnaire containing images in white light imaging (WLI) mode and LCI mode was used to assess the trainees' ability to determine the status of H. pylori infection by observing endoscopic mucosal manifestations before and after learning the Kyoto Classification of Gastritis. SPSS 26.0 software was used for statistical analysis. Results The analysis of expert endoscopists on the Kyoto Classification of Gastritis showed that gastroscopic mucosal manifestations including mucosal atrophy, hematin, mucosal swelling, and sticky mucus had an impact on the judgment of H. pylori infection (P < 0.05). After training, the total questionnaire score of GPs was improved (3.48 vs 4.45, P < 0.05), and there was no difference in the questionnaire completion time between GPs and standard gastroenterologists (SGs). After training, the questionnaire score of GPs based on images in WLI and LCI modes was improved. The score of the LCI mode was higher than that of the WLI mode (LCI: 1.45 vs 2.14, P < 0.05, WLI: 2.04 vs 2.31, P = 0.355). Conclusions Kyoto Classification of Gastritis combined with LCI improves the ability of GPs to diagnose H. pylori infection through endoscopic images.