Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy - Shi, Yanting; Wei, Ning; Wang, Kunhong; Wu, Jingjing; Tao, Tao; Li, Na; Lv, Bing (Frontiers in Oncology, 2023)
Background: Chronic atrophic gastritis (CAG) is a precancerous disease. It is not easy to detect CAG under endoscopy. Improving the detection rate of CAG under endoscopy is essential to slow down or interrupt the occurrence of gastric cancer. This study aims to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the detection rate of CAG under endoscopy. Methods: We collected 10,961 endoscopic images and 118 video clips from 4,050 patients. For model training and testing, we divided them into two groups based on pathological results, CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) deep learning networks on CAG recognition and selected one of them for further improvement. The improved network is called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps. Results: After fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.59% in the external test set, and 96.23%, 89.23%, and 92.37% in the video test set, which were higher than those of the three endoscopists. Conclusions: The CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.