An Explainable Artificial Intelligence System for Diagnosing Infection Under Endoscopy: a Case-control Study

Background: Changes in gastric mucosa caused by () infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that computer-aided diagnosis (CAD) systems have great potential in the diagnosis of infection, their explainability remains a challenge.

Objective: We aim to develop an explainable artificial intelligence system for diagnosing infection (EADHI) and giving diagnostic basis under endoscopy.

Design: A case-control study.

Methods: We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for infection. EADHI's performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing infection.

Results: The system extracted mucosal features for diagnosing infection with an overall accuracy of 78.3% [95% confidence interval (CI): 76.2-80.3]. The accuracy of EADHI for diagnosing infection (91.1%, 95% CI: 85.7-94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7-21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6-95.7) in external test. Mucosal edema was the most important diagnostic feature for positive, while regular arrangement of collecting venules was the most important negative feature.

Conclusion: The EADHI discerns gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs.

Plain Language Summary: () is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% [95% confidence interval (CI): 85.7-94.6], which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7-21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6-95.7) in external tests. The EADHI discerns gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs.