Lianlian Wu1,2,3, Xinqi He1,2,3, Mei Liu4, Huaping Xie4, Ping An1,2,3, Jun Zhang1,2,3, Heng Zhang5, Yaowei Ai6, Qiaoyun Tong7, Mingwen Guo6, Manling Huang5, Cunjin Ge7, Zhi Yang7, Jingping Yuan8, Jun Liu1,3, Wei Zhou1,2,3, Xiaoda Jiang1,2,3, Xu Huang1,2,3, Ganggang Mu1,2,3, Xinyue Wan1,2,3, Yanxia Li1,2,3, Hongguang Wang9, Yonggui Wang10, Hongfeng Zhang11, Di Chen1,2,3, Dexin Gong1,2,3, Jing Wang1,2,3, Li Huang1,2,3, Jia Li1,2,3, Liwen Yao1,2,3, Yijie Zhu1,2,3, Honggang Yu1,2,3. 1. Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China. 2. Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China. 3. Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China. 4. Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 5. Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 6. Department of Gastroenterology, The People's Hospital of China Three Gorges University/The First People's Hospital of Yichang, Yichang, China. 7. Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China. 8. Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China. 9. Department of Gastroenterology, Jilin People's Hospital, Jilin, China. 10. School of Geography and Information Engineering, China University of Geosciences, Wuhan, China. 11. Department of Pathology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract
BACKGROUND: Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS: 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS: In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time. Thieme. All rights reserved.
BACKGROUND: Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS: 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS: In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time. Thieme. All rights reserved.
Authors: Fei Kuang; Juan Du; Mengjia Zhou; Xiangdong Liu; Xinchen Luo; Yong Tang; Bo Li; Song Su Journal: Front Oncol Date: 2022-06-10 Impact factor: 5.738
Authors: Radu-Alexandru Vulpoi; Mihaela Luca; Adrian Ciobanu; Andrei Olteanu; Oana-Bogdana Barboi; Vasile Liviu Drug Journal: Diagnostics (Basel) Date: 2022-04-08
Authors: Thomas Y T Lam; Max F K Cheung; Yasmin L Munro; Kong Meng Lim; Dennis Shung; Joseph J Y Sung Journal: J Med Internet Res Date: 2022-08-25 Impact factor: 7.076