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小肠胶囊内镜中人工智能未满足的需求

Unmet Needs of Artificial Intelligence in Small Bowel Capsule Endoscopy.

作者信息

Piccirelli Stefania, Salvi Daniele, Pugliano Cecilia Lina, Tettoni Enrico, Facciorusso Antonio, Rondonotti Emanuele, Mussetto Alessandro, Fuccio Lorenzo, Cesaro Paola, Spada Cristiano

机构信息

Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.

Department of Experimental Medicine, Università del Salento, 73100 Lecce, Italy.

出版信息

Diagnostics (Basel). 2025 Apr 25;15(9):1092. doi: 10.3390/diagnostics15091092.

DOI:10.3390/diagnostics15091092
PMID:40361910
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12071857/
Abstract

Small bowel capsule endoscopy (SBCE) has emerged in the past two decades as the cornerstone for assessing small bowel disorders, and its use is supported by several guidelines. However, there are several limitations, such as the considerable time required for gastroenterologists to review these videos and reach a diagnosis. To address these limitations, researchers have explored the integration of artificial intelligence in the interpretation of these videos. In our review, we explore the evolving and emerging role of artificial intelligence in SBCE and examine the latest advancements and ongoing studies in these areas, aiming at overcoming current limitations.

摘要

在过去二十年中,小肠胶囊内镜检查(SBCE)已成为评估小肠疾病的基石,并且多项指南都支持其使用。然而,它存在一些局限性,比如胃肠病学家查看这些视频并做出诊断需要相当长的时间。为了解决这些局限性,研究人员探索了将人工智能整合到这些视频的解读中。在我们的综述中,我们探讨了人工智能在小肠胶囊内镜检查中不断演变和新兴的作用,并研究了这些领域的最新进展和正在进行的研究,旨在克服当前的局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b25/12071857/77d8baa2cb27/diagnostics-15-01092-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b25/12071857/4a97717f0412/diagnostics-15-01092-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b25/12071857/77d8baa2cb27/diagnostics-15-01092-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b25/12071857/4a97717f0412/diagnostics-15-01092-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b25/12071857/77d8baa2cb27/diagnostics-15-01092-g002.jpg

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本文引用的文献

1
Performance measures for small-bowel endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative - Update 2025.小肠内镜检查的性能指标:欧洲胃肠内镜学会(ESGE)质量改进倡议——2025年更新
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Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.使用汇总机器学习算法实现自动小肠胶囊内镜报告:SUM UP研究。
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A new artificial intelligence system for both stomach and small-bowel capsule endoscopy.
一种用于胃部和小肠胶囊内镜的新型人工智能系统。
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Lancet Digit Health. 2024 May;6(5):e345-e353. doi: 10.1016/S2589-7500(24)00048-7.
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Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy.卷积神经网络设计作为胶囊内镜中小肠清洁自动分类的深度学习工具。
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Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2022.小肠胶囊内镜检查和设备辅助小肠镜检查在小肠疾病诊断和治疗中的应用:欧洲胃肠内镜学会(ESGE)指南 - 2022年更新版
Endoscopy. 2023 Jan;55(1):58-95. doi: 10.1055/a-1973-3796. Epub 2022 Nov 24.
7
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Sci Rep. 2022 Oct 29;12(1):18265. doi: 10.1038/s41598-022-23181-1.
8
Inter/Intra-Observer Agreement in Video-Capsule Endoscopy: Are We Getting It All Wrong? A Systematic Review and Meta-Analysis.视频胶囊内镜检查中观察者间/观察者内一致性:我们全错了吗?一项系统评价和荟萃分析
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Front Robot AI. 2022 Sep 2;9:896028. doi: 10.3389/frobt.2022.896028. eCollection 2022.
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