Abstract
AbstractGastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
Funder
National Key Research and Development Projec
National Major Scientific Research Instrument Development Projec
Robotics Institute of Zhejiang University
National Key Research and Development Project
Key Research and Development Plan of Zhejiang Province
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
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