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从数据到洞察:人工智能如何变革小肠内镜检查?

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

作者信息

Mota Joana, Almeida Maria João, Mendes Francisco, Martins Miguel, Ribeiro Tiago, Afonso João, Cardoso Pedro, Cardoso Helder, Andrade Patrícia, Ferreira João, Mascarenhas Miguel, Macedo Guilherme

机构信息

Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.

WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal.

出版信息

Diagnostics (Basel). 2024 Jan 29;14(3):291. doi: 10.3390/diagnostics14030291.

DOI:10.3390/diagnostics14030291
PMID:38337807
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10855436/
Abstract

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

摘要

胶囊内镜和小肠镜在处理各种小肠病变中的作用已得到充分确立。然而,它们的广泛应用主要受到其冗长的阅片时间的阻碍。因此,受克服一些主要局限性并提高医疗效率同时保持高精度水平这一前景的驱动,在这些诊断和治疗程序中采用人工智能(AI)的兴趣日益浓厚。在过去二十年中,AI在胃肠病学中的适用性一直在增加,主要是因为其强大的成像组件。如今,有大量使用AI的研究,特别是使用卷积神经网络的研究,证明了AI在这些内镜技术中的潜在应用,并取得了显著成果。这些发现表明,AI在胃肠病学疾病管理中扩大其影响力有充足的机会,并且在未来能够推动临床活动发生改变游戏规则的变革。本综述概述了AI在小肠研究范围内的当前技术水平,特别关注胶囊内镜和小肠镜。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/2dd1fbb157df/diagnostics-14-00291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/2b6a2a79fa9b/diagnostics-14-00291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/93ba07413b72/diagnostics-14-00291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/c9381c9c8023/diagnostics-14-00291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/2dd1fbb157df/diagnostics-14-00291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/2b6a2a79fa9b/diagnostics-14-00291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/93ba07413b72/diagnostics-14-00291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/c9381c9c8023/diagnostics-14-00291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b7/10855436/2dd1fbb157df/diagnostics-14-00291-g004.jpg

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Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care.智能内镜检查是更环保的内镜检查:利用人工智能和区块链技术推动消化健康护理的可持续发展。
Diagnostics (Basel). 2023 Dec 8;13(24):3625. doi: 10.3390/diagnostics13243625.
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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.
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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。
Medicina (Kaunas). 2023 Apr 21;59(4):810. doi: 10.3390/medicina59040810.
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The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents.人工智能在消化保健领域的前景及其带来的生物伦理挑战。
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