Chronic atrophic gastritis (CAG) is a precancerous condition. It is not easy to detect CAG in endoscopy. Improving the detection rate of CAG under endoscopy is essential to reduce or interrupt the occurrence of gastric cancer. This study aimed to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the CAG detection rate during endoscopy.
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 the pathological results: CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) DL networks for CAG recognition and selected one of them for further improvement. The improved network was called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps.
After fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.5% in the external test set and 96.23%, 89.23%, and 92.37% in the video test set, respectively, which were higher than those of the three endoscopists.
The CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.