Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Issue 5 (May 2020)

• Record Type: Journal Article
• Authors: Zhang, Yaqiong; Li, Fengxia; Yuan, Fuqiang; Zhang, Kai; Huo, Lijuan; Dong, Zichen; Lang, Yiming; Zhang, Yapeng; Wang, Meihong; Gao, Zenghui; Qin, Zhenzhen; Shen, Leixue
• Is Part Of: Digestive and liver disease. Volume 52:Issue 5(2020)
• Page Start: 566
• Page End: 572
• Publication Date: 2020-05
• Subjects: Artificial intelligence -- Chronic atrophic gastritis -- Convolutional neural network -- Stomach cancer; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; 616.33005
• DOI: 10.1016/j.dld.2019.12.146
• Languages: English
• ISSNs: 1590-8658

Abstract

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.