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深度学习在胃镜图像中的应用：临床医师的计算机辅助技术。

Deep learning for gastroscopic images: computer-aided techniques for clinicians.

机构信息

Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.

Hangzhou Center for Medical Device Quality Supervision and Testing, CFDA, Hangzhou, 310000, People's Republic of China.

出版信息

Biomed Eng Online. 2022 Feb 11;21(1):12. doi: 10.1186/s12938-022-00979-8.

DOI:10.1186/s12938-022-00979-8
PMID:35148764
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8832738/
Abstract

Gastric 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.

摘要

胃疾病是全球范围内的一个主要健康问题。胃镜检查是筛查和诊断许多胃部疾病的主要方法和金标准。然而，一些因素，如内镜医生的经验和疲劳，限制了其性能。随着深度学习技术的最新进展，越来越多的研究已经开始使用这项技术在实时胃镜检查期间提供现场协助。本文综述了深度学习在克服与疾病相关和非疾病相关胃镜检查挑战中的最新应用。前者旨在帮助内镜医生在胃镜视野中发现病变并对其进行特征描述。后者的目的是避免因帧质量差、胃镜检查覆盖不完全等原因而漏诊病变，从而提高胃镜检查的质量。本研究旨在为医生提供技术指导和全面的视角，以了解胃镜检查中的深度学习技术。本文讨论了在深度学习技术的临床应用之前需要解决的一些关键问题，以及深度学习在与疾病相关和非疾病相关的胃镜检查中的未来应用方向。

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3
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4
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5
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6
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7
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7
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