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Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial

Authors :
Wu, Lianlian
Shang, Renduo
Sharma, Prateek
Zhou, Wei
Liu, Jun
Yao, Liwen
Dong, Zehua
Yuan, Jingping
Zeng, Zhi
Yu, Yuanjie
He, Chunping
Xiong, Qiutang
Li, Yanxia
Deng, Yunchao
Cao, Zhuo
Huang, Chao
Zhou, Rui
Li, Hongyan
Hu, Guiying
Chen, Yiyun
Wang, Yonggui
He, Xinqi
Zhu, Yijie
Yu, Honggang
Source :
The Lancet Gastroenterology & Hepatology; September 2021, Vol. 6 Issue: 9 p700-708, 9p
Publication Year :
2021

Abstract

White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among endoscopists. Here, we aimed to evaluate the effect of an artificial intelligence (AI) system designed to detect focal lesions and diagnose gastric neoplasms on reducing the miss rate of gastric neoplasms in clinical practice.

Details

Language :
English
ISSN :
24681253
Volume :
6
Issue :
9
Database :
Supplemental Index
Journal :
The Lancet Gastroenterology & Hepatology
Publication Type :
Periodical
Accession number :
ejs57215055
Full Text :
https://doi.org/10.1016/S2468-1253(21)00216-8