In Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND : Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools.
AIMS : To develop an artificial intelligence (AI)-assisted EGD system able to automatically monitor blind spots in real-time.
METHODS : An AI-based system, called the Intelligent Detection Endoscopic Assistant (IDEA), was developed using a deep convolutional neural network (DCNN) and long short-term memory (LSTM). The performance of IDEA for recognition of gastric sites in images and videos was evaluated. Primary outcomes included diagnostic accuracy, sensitivity, and specificity.
RESULTS : A total of 170,297 images and 5779 endoscopic videos were collected to develop the system. As the test group, 3100 EGD images were acquired to evaluate the performance of DCNN in recognition of gastric sites in images. The sensitivity, specificity, and accuracy of DCNN were determined as 97.18%,99.91%, and 99.83%, respectively. To assess the performance of IDEA in recognition of gastric sites in EGD videos, 129 videos were used as the test group. The sensitivity, specificity, and accuracy of IDEA were 96.29%,93.32%, and 95.30%, respectively.
CONCLUSIONS : IDEA achieved high accuracy for recognition of gastric sites in real-time. The system can be applied as a powerful assistant tool for monitoring blind spots during EGD.
Li Yan-Dong, Zhu Shu-Wen, Yu Jiang-Ping, Ruan Rong-Wei, Cui Zhao, Li Yi-Ting, Lv Mei-Chao, Wang Huo-Gen, Chen Ming, Jin Chao-Hui, Wang Shi
Artificial intelligence, Deep convolutional neural network, Endoscopy, Long short-term memory