Braverman-Jaiven Dalia, Manfredi Luigi
Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee, United Kingdom.
Front Robot AI. 2024 Oct 21;11:1453194. doi: 10.3389/frobt.2024.1453194. eCollection 2024.
Inflammatory bowel disease (IBD) causes chronic inflammation of the colon and digestive tract, and it can be classified as Crohn's disease (CD) and Ulcerative colitis (UC). IBD is more prevalent in Europe and North America, however, since the beginning of the 21st century it has been increasing in South America, Asia, and Africa, leading to its consideration as a worldwide problem. Optical colonoscopy is one of the crucial tests in diagnosing and assessing the progression and prognosis of IBD, as it allows a real-time optical visualization of the colonic wall and ileum and allows for the collection of tissue samples. The accuracy of colonoscopy procedures depends on the expertise and ability of the endoscopists. Therefore, algorithms based on Deep Learning (DL) and Convolutional Neural Networks (CNN) for colonoscopy images and videos are growing in popularity, especially for the detection and classification of colorectal polyps. The performance of this system is dependent on the quality and quantity of the data used for training. There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. As artificial intelligence (AI) grows in popularity there is a growing interest in the use of these algorithms for diagnosing and classifying the IBDs and managing their progression. To tackle this, more annotated colonoscopy images and videos will be required for the training of new and more reliable AI algorithms. This article discusses the current challenges in the early detection of IBD, focusing on the available AI algorithms, and databases, and the challenges ahead to improve the detection rate.
炎症性肠病（IBD）会导致结肠和消化道的慢性炎症，可分为克罗恩病（CD）和溃疡性结肠炎（UC）。IBD在欧洲和北美更为普遍，然而，自21世纪初以来，它在南美洲、亚洲和非洲呈上升趋势，这使其被视为一个全球性问题。光学结肠镜检查是诊断和评估IBD进展及预后的关键检查之一，因为它能实时光学观察结肠壁和回肠，并可采集组织样本。结肠镜检查程序的准确性取决于内镜医师的专业知识和能力。因此，基于深度学习（DL）和卷积神经网络（CNN）的用于结肠镜检查图像和视频的算法越来越受欢迎，特别是用于结直肠息肉的检测和分类。该系统的性能取决于用于训练的数据的质量和数量。有几个用于内镜图像和视频的公开数据集，但其中大多数仅专门用于息肉。使用DL算法检测IBD仍处于起步阶段，大多数研究基于评估UC的严重程度。随着人工智能（AI）越来越受欢迎，人们对使用这些算法诊断和分类IBD以及管理其进展的兴趣日益浓厚。为了解决这个问题，训练更新、更可靠的AI算法将需要更多带注释的结肠镜检查图像和视频。本文讨论了IBD早期检测中的当前挑战，重点关注可用的AI算法、数据库以及提高检测率面临的挑战。
