Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea.
Gut Liver. 2019 Jan 11;13(4):388-393. doi: 10.5009/gnl18384.
Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deep-learning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings.
人工智能可能会执行目前由人类执行的若干角色，预计在不久的将来，基于人工智能的医学将在胃肠病学实践中得到采用。基于医学影像的诊断，如病理学、放射学和内镜检查，预计将成为人工智能首先影响的医学领域。最近有报道称，卷积神经网络（一种具有多层感知器的深度学习方法，旨在使用最少的预处理）在包括食管胃十二指肠镜、结肠镜和胶囊内镜在内的内镜领域非常有益。一个基于卷积神经网络的诊断程序被要求识别食管胃十二指肠镜图像中的解剖位置、感染和胃癌；检测和分类结肠直肠息肉；识别乳糜泻和钩虫；以及对胶囊内镜图像进行小肠运动特征描述。人工智能有望通过自动检测和分类病变帮助内镜医生提供更准确的诊断；因此，内镜医生关注这项新技术至关重要。在这篇综述中，我们描述了人工智能对胃肠病学的影响，特别关注基于内镜检查结果的自动诊断。
