Peng Yuan,1,* Zhong-Hua Ma,2,* Yan Yan,1,* Shi-Jie Li,2 Jing Wang,2 Qi Wu1 

1State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China; 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Qi Wu, State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China, Email wuqi1973@126.com

Background: A full examination of gastrointestinal tract is an essential prerequisite for effectively detecting gastrointestinal lesions. However, there is a lack of efficient tools to analyze and recognize gastric anatomy locations, preventing the complete portrayal of entire stomach. This study aimed to evaluate the effectiveness of artificial intelligence in identifying gastric anatomy sites by analyzing esophagogastroduodenoscopy images.
Methods: Using endoscopic images, we proposed a system called the Artificial Intelligence of Medicine (AIMED) through convolutional neural networks and MobileNetV3-large. The performance of artificial intelligence in the recognition of anatomic sites in esophagogastroduodenoscopy images was evaluated by considering many cases. Primary outcomes included diagnostic accuracy, sensitivity, and specificity.
Results: A total of 160,308 images from 27 categories of the upper endoscopy anatomy classification were included in this retrospective research. As a test group, 16031 esophagogastroduodenoscopy images with 27 categories were used to evaluate AIMED’s performance in identifying gastric anatomy sites. The convolutional neural network’s accuracy, sensitivity, and specificity were determined to be 99.40%, 91.85%, and 99.69%, respectively.
Conclusion: The AIMED system achieved high accuracy with regard to recognizing gastric anatomy sites, and it could assist the operator in enhancing the quality control of the used endoscope. Moreover, it could contribute to a more standardized endoscopic performance. Overall, our findings prove that artificial-intelligence-based systems can be indispensable to the endoscopic revolution (Clinical trial registration number: NCT04384575 (12/05/2020)).

Keywords: artificial intelligence, convolutional neural network, esophagogastroduodenoscopy, quality control