Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?
Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?
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DOI：
10.1016/j.tgie.2019.150634
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发表时间：
2020-04-01
影响因子：
2.4
通讯作者：
Wallace, Michael B.
中科院分区：
文献类型：
--
作者：
Hoogenboom, Sanne A.;Bagci, Ulas;Wallace, Michael B.
背景：人工智能（AI）在医疗领域的兴趣和应用呈指数级增长。深度学习是机器学习下人工智能的一个特殊分支，它通过学习识别复杂的特征本身，而不依赖于先验的人类生成的分类规则，开启了人工智能的革命。近年来，人工智能在胃肠内窥镜检查中的一些应用不断涌现。计算机辅助检测和诊断可能是解决内窥镜检查中操作员依赖性的解决方案。在这篇综述中，我们的目标是向胃肠病学家介绍与人工智能相关的复杂术语、人工智能辅助内窥镜检查的现状及其未来方向。方法：我们使用人工智能、深度学习、计算机辅助检测和诊断以及胃肠内窥镜检查等关键词，在 MED-LINE 和 PUBMED 上进行了截至 2019 年 5 月的文献检索，查找相关文章。结果：文献中描述的人工智能在内窥镜检查中的应用包括结直肠息肉检测和分类、癌症侵袭性评估、视频胶囊内窥镜检查、食管癌和胃癌检测以及幽门螺杆菌胃炎。结论：人工智能辅助内窥镜检查是一个不断发展的领域，该主题的最新创新和研究前景广阔。通过启动人工智能辅助内窥镜检查的首次前瞻性研究，我们已经在人工智能道路上迈出了重要的第一步，以尽量减少选择偏差和人工智能模型过度拟合的风险。未来的研究将调查人工智能辅助内窥镜检查是否会改善我们当前的内窥镜检查能力。 (c) 2019 Elsevier Inc. 保留所有权利。
Background: Artificial intelligence (AI) is exponentially gaining interest and utilization in medical fields. Deep learning, a particular branch of AI under machine learning, started a revolution in AI by learning to recognize complex features itself, without dependence on a priori human-generated rules of classification. In recent years, several applications of AI are emerging in gastrointestinal endoscopy. Computer-aided detection and diagnosis might be the solution for the operator dependency in endoscopy. In this review, we aim to provide an introduction for gastroenterologists to the complex terminology that is linked to AI, the current state of play in AI-assisted endoscopy, and its future directions. Methods: We performed a literature search on MED-LINE and PUBMED through May 2019 for relevant articles using keywords as AI, deep learning, computer-aided detection and diagnosis, and gastrointestinal endoscopy. Results: AI-applications in endoscopy described in the literature included colorectal polyp detection and classification, assessment of cancer inva-siveness, video capsule endoscopy, detection of esophageal and gastric cancer, and Helicobacter pylori gastritis. Conclusion: AI-assisted endoscopy is a strongly evolving field and recent innovations and research on this sub-ject are promising. Initial important steps along the AI-road have been taken by initiating the first prospective studies on AI-assisted endoscopy to minimize the risk of selection bias and overfitting of the AI-models. Future research will investigate if AI-assisted endoscopy will refine our current endoscopic abilities. (c) 2019 Elsevier Inc. All rights reserved.