Stratification of gastric cancer risk using a deep neural network

JGH Open : an Open Access Journal of Gastroenterology and Hepatology
Hiroko NakahiraTomohiro Tada

Abstract

Stratifying gastric cancer (GC) risk and endoscopy findings in high-risk individuals may provide effective surveillance for GC. We developed a computerized image- analysis system for endoscopic images to stratify the risk of GC. The system was trained using images taken during endoscopic examinations with non-magnified white-light imaging. Patients were classified as high-risk (patients with GC), moderate-risk (patients with current or past Helicobacter pylori infection or gastric atrophy), or low-risk (patients with no history of H. pylori infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high-, moderate-, and low-risk groups, respectively. Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and H. pylori serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low-, moderate-, and high risk, respectively. The prevalence of GC in the low-, moderate-, and hig...Continue Reading

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Citations

Oct 3, 2020·Frontiers in Molecular Biosciences·Xiao-Jing ShiBoyang Ji
Oct 8, 2020·World Journal of Gastroenterology : WJG·Peng-Hui NiuYing-Tai Chen
Feb 25, 2021·Journal of Gastroenterology and Hepatology·Dennis L Shung
Feb 18, 2021·Current Opinion in Otolaryngology & Head and Neck Surgery·Alberto PadernoCesare Piazza

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Methods Mentioned

BETA
biopsy

Software Mentioned

StatView
EZR
Caffe

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