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炎症性肠病的下一代内镜检查

Next-Generation Endoscopy in Inflammatory Bowel Disease.

作者信息

Zammarchi Irene, Santacroce Giovanni, Iacucci Marietta

机构信息

APC Microbiome Ireland, College of Medicine and Health, University College Cork, T12 R229 Cork, Ireland.

出版信息

Diagnostics (Basel). 2023 Jul 31;13(15):2547. doi: 10.3390/diagnostics13152547.

Abstract

Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.

摘要

内镜愈合被认为是炎症性肠病(IBD)的主要治疗目标。然而,内镜缓解可能并不反映组织学缓解,而组织学缓解对于实现良好的长期预后至关重要。新的先进技术的发展彻底改变了IBD评估和管理领域。这些工具可以准确评估血管和黏膜特征,使内镜检查更接近组织学检查。此外,它们可以提高对IBD相关发育异常的检测和特征描述能力。鉴于观察者间差异这一持续存在的挑战,有必要采用更标准化的内镜检查方法,而人工智能(AI)的整合有望解决这一局限性。此外,尽管分子内镜仍处于起步阶段,但它是预测治疗反应的一种有前景的工具。本综述概述了先进的内镜技术,包括基于染料和无染料的染色内镜检查,以及基于探头的共聚焦激光内镜显微镜和内镜细胞检查的体内组织学检查。讨论了这些工具对IBD管理的显著贡献,特别是与AI整合时的贡献。特别关注它们在改善疾病评估、检测和IBD相关病变的特征描述以及预测疾病相关结局方面的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd43/10417286/9985e1050a8a/diagnostics-13-02547-g001.jpg

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