■基于人工智能(AI)的应用程序已经改变了多个行业,并广泛用于各种消费产品和服务中。在医学上,人工智能主要用于图像分类和自然语言处理,并有很大的潜力影响基于图像的专业,如放射学,病理学,和胃肠病学(GE)。本文件回顾了人工智能在通用电气中的应用报告,专注于内窥镜图像分析。
■通过使用机器学习等关键字,在MEDLINE数据库中搜索了相关文章,直到2020年5月,深度学习,人工智能,计算机辅助诊断,卷积神经网络,胃肠内窥镜检查,和内窥镜图像分析。还评估了检索到的文章的参考文献和引文,以确定相关研究。该手稿由2位作者起草,并由美国胃肠内窥镜检查技术委员会的成员亲自审查,随后由美国胃肠内窥镜检查学会理事会审查。
■深度学习技术(如卷积神经网络)已在胃肠道内窥镜检查的多个领域中使用,包括结直肠息肉的检测和分类,分析诊断幽门螺杆菌感染的内镜图像,早期胃癌的检测和深度评估,Barrett食管发育不良,并检测无线胶囊内窥镜图像中的各种异常。
■在多种胃肠道内窥镜应用中实施AI技术有可能有利地改变临床实践并提高当前诊断方法的效率和准确性。
UNASSIGNED: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.
UNASSIGNED: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.
UNASSIGNED: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett\'s esophagus, and detection of various abnormalities in wireless capsule endoscopy images.
UNASSIGNED: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.