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人工智能在上消化道内镜检查中的重要性。

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.

作者信息

Popovic Dusan, Glisic Tijana, Milosavljevic Tomica, Panic Natasa, Marjanovic-Haljilji Marija, Mijac Dragana, Stojkovic Lalosevic Milica, Nestorov Jelena, Dragasevic Sanja, Savic Predrag, Filipovic Branka

机构信息

Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia.

Department of Gastroenterology, Clinical Hospital Center "Dr Dragisa Misovic-Dedinje", 11000 Belgrade, Serbia.

出版信息

Diagnostics (Basel). 2023 Sep 5;13(18):2862. doi: 10.3390/diagnostics13182862.


DOI:10.3390/diagnostics13182862
PMID:37761229
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10528171/
Abstract

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

摘要

近年来,人工智能(AI)在医学领域的应用越来越受到关注,尤其是在应用可视化方法的专业领域。人工智能被定义为计算机实现人类认知性能的能力,这是通过使计算机“学习”来完成的。这可以通过机器学习和深度学习两种方式进行。深度学习是一个复杂的学习系统,涉及人工神经网络的应用,其算法模仿人类的学习形式。上消化道内镜检查可对食管、胃和十二指肠进行检查。除了内镜设备的质量和患者准备情况外,上消化道内镜检查的效果还取决于内镜医师的经验和知识。人工智能在内镜检查中的应用是指计算机辅助检测和更复杂的计算机辅助诊断。人工智能在上消化道内镜检查中的应用旨在提高癌前病变和恶性病变的检测率,特别关注巴雷特食管发育异常的早期检测、食管癌和胃癌的早期检测以及感染的检测。人工智能减轻了内镜医师的工作量,不受人为因素影响,并提高了内镜检查方法的诊断准确性和质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/819c/10528171/e974802cae3a/diagnostics-13-02862-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/819c/10528171/e974802cae3a/diagnostics-13-02862-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/819c/10528171/e974802cae3a/diagnostics-13-02862-g001.jpg

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The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.

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本文引用的文献

[1]
Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system.

Discov Oncol. 2023-5-19

[2]
Towards a robust and compact deep learning system for primary detection of early Barrett's neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set.

United European Gastroenterol J. 2023-5

[3]
Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

Dig Liver Dis. 2023-5

[4]
Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: a multicenter study.

Gastrointest Endosc. 2023-5

[5]
Development and validation of artificial neural networks model for detection of Barrett's neoplasia: a multicenter pragmatic nonrandomized trial (with video).

Gastrointest Endosc. 2023-3

[6]
EFFICACY ANALYSIS OF ENDOSCOPIC SUBMUCOSAL DISSECTION FOR THE EARLY GASTRIC CANCER AND PRECANCEROUS LESIONS.

Arq Gastroenterol. 2022

[7]
Implementation of artificial intelligence in upper gastrointestinal endoscopy.

DEN Open. 2022-3-15

[8]
Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice.

Diagnostics (Basel). 2022-5-21

[9]
[Establishment and clinical validation of an artificial intelligence YOLOv51 model for the detection of precancerous lesions and superficial esophageal cancer in endoscopic procedure].

Zhonghua Zhong Liu Za Zhi. 2022-5-23

[10]
Global variations in diagnostic guidelines for Barrett's esophagus.

Dig Endosc. 2022-11

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