Yaqiong Zhang1, Fengxia Li2, Fuqiang Yuan3, Kai Zhang4, Lijuan Huo5, Zichen Dong4, Yiming Lang4, Yapeng Zhang6, Meihong Wang1, Zenghui Gao1, Zhenzhen Qin1, Leixue Shen4. 1. Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China. 2. Department of Gastroenterology, Shanxi Provincial People's Hospital, Taiyuan, China. Electronic address: doctorlfx@163.com. 3. Baidu Online Network Technology (Beijing) Corporation, Beijing, China. 4. School of Computer Science and Technology, Xidian University, Xi'an, China. 5. Department of Gastroenterology, The First Hospital of Shanxi Medical University, Taiyuan, China. 6. Fenyang College of Shanxi Medical University, Fenyang, China.
Abstract
BACKGROUND: The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS: This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS: We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. RESULTS: The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. CONCLUSION: Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.
BACKGROUND: The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS: This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS: We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. RESULTS: The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. CONCLUSION: Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.
Authors: Rahul Pannala; Kumar Krishnan; Joshua Melson; Mansour A Parsi; Allison R Schulman; Shelby Sullivan; Guru Trikudanathan; Arvind J Trindade; Rabindra R Watson; John T Maple; David R Lichtenstein Journal: VideoGIE Date: 2020-11-09
Authors: Jingting Luo; Yuning Chen; Yuhang Yang; Kai Zhang; Yueming Liu; Hanqing Zhao; Li Dong; Jie Xu; Yang Li; Wenbin Wei Journal: Front Med (Lausanne) Date: 2022-01-21