Purpose of review 

The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients.

Recent findings 

Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment.

Summary 

Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.

中文翻译：

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炎症性肠病的机器和深度学习

审查目的 

炎症性肠病(IBD)的治疗随着生物制剂的引入和广泛采用而不断发展；然而，机器学习和深度学习等人工智能技术的出现为IBD治疗带来了另一个分水岭。过去 10 年来，IBD 研究中对这些方法的兴趣有所增加，它们为 IBD 患者提供了一条获得更好临床结果的有希望的途径。

最近的发现 

由于数据量巨大且需要手动解释数据，开发新工具来评估 IBD 并为临床管理提供信息具有挑战性。最近，机器和深度学习模型已被用于通过高精度自动审查多种诊断方式的数据来简化 IBD 的诊断和评估。这些方法减少了临床医生手动审查数据以制定评估所花费的时间。

概括 

医学界对机器和深度学习的兴趣与日俱增，这些方法有望彻底改变我们治疗 IBD 的方式。在这里，我们重点介绍使用这些技术评估 IBD 的最新进展，并讨论如何利用它们来改善临床结果。