A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score

Abstract

Objective: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to the definition of Kyoto Gastritis Score, with the goal of improving early gastric cancer detection and reducing gastric cancer mortality.

Methods: In this retrospective study, 29013 gastric endoscopy images were collected and carefully annotated into five categories according to the Kyoto Gastritis Score, i.e. atrophy (A), diffuse redness (DR), enlarged folds (H), intestinal metaplasia (IM), and nodularity (N). As a multi-label recognition task, we propose a deep learning approach composed of five GAM-EfficientNet models, each performing a multiple classification to quantify gastroscopic manifestations, i.e. no presentation or the severity score 0-2. This approach was compared with endoscopists of varying years of experience in terms of accuracy, specificity, precision, recall, and F1 score.

Results: The approach demonstrated good performance in identifying the five manifestations of the Kyoto Gastritis Score, with an average accuracy, specificity, precision, recall, and F1 score of 78.70%, 91.92%, 80.23%, 78.70%, and 0.78, respectively. The average performance of five experienced endoscopists was 72.63%, 90.00%, 77.68%, 72.63%, and 0.73, while that of five less experienced endoscopists was 66.60%, 87.44%, 70.88%, 66.60%, and 0.66, respectively. The sample t-test indicates that the approach's average accuracy, specificity, precision, recall, and F1 score for identifying the five manifestations were significantly higher than those of less experienced endoscopists, experienced endoscopists, and all endoscopists on average (p < 0.05).

Conclusion: Our study demonstrates the potential of deep learning approaches on gastric manifestation recognition over junior, even senior endoscopists. Thus, the deep learning approach holds potential as an auxiliary tool, although prospective validation is still needed to assess its clinical applicability.

Keywords: Kyoto Gastritis Score; deep learning; gastric cancer; gastroscopic manifestations.

Figure 1.

Flow chart of the experiment design.

Figure 2.

Confusion matrix of prediction in the test dataset for DLKGS.

Figure 3.

ROC and P-R curves of the five models.

Figure 4.

Representative examples of different manifestations with different scores (left) along with their feature heatmaps outputted by Grad-CAM for DLKGS (right). Since each image is annotated with 5 labels (each corresponding to one manifestation), identical images exist to represent different manifestations. For example, the image illustrating score 0 for manifestation A is also a representative image showing no presentation for manifestation DR. All representative images for A, DR, H, and N are WLI images, whereas for IM, both WLI images and NBI images are shown

Figure 5.

Comparison of gastroscopic manifestation recognition ability between DLKGS and endoscopists.