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胶囊内镜在炎症性肠病中的应用:全肠胶囊内镜和人工智能的应用。

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.

机构信息

Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, Israel.

Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.

出版信息

Gut Liver. 2023 Jul 15;17(4):516-528. doi: 10.5009/gnl220507. Epub 2023 Jun 12.

Abstract

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn's disease (CD). In 2017, the panenteric capsule (PillCam Crohn's system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.

摘要

视频胶囊内镜(VCE)已被证实可准确诊断小肠炎症,并预测克罗恩病(CD)患者的未来临床发作。2017 年,首次引入全肠胶囊(PillCam Crohn's 系统),可可靠评估整个小肠和大肠。在可行的单一程序中同时可视化胃肠道的两部分具有重要意义,为 CD 患者带来了希望,能够确定疾病的范围和严重程度,并有可能优化疾病管理。近年来,机器学习在 VCE 中的应用得到了很好的研究,证明其在检测各种胃肠道病变方面具有出色的性能和高精度,其中包括炎症性肠病病变。人工神经网络模型的使用已被证明可准确检测/分类和分级 CD 病变,并缩短 VCE 阅读时间,从而减少繁琐的过程,降低漏诊的可能性,并更好地预测临床结果。然而,前瞻性和真实世界的研究对于在真实的炎症性肠病实践中精确检查人工智能应用至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea72/10352070/bb009081205e/gnl-17-4-516-f1.jpg

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