Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
Curr Opin Gastroenterol. 2023 Jul 1;39(4):294-300. doi: 10.1097/MOG.0000000000000945. Epub 2023 May 8.
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.
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.
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.
随着生物制剂的引入和广泛应用，炎症性肠病（IBD）的治疗已经发生了变化；然而，机器学习和深度学习等人工智能技术的出现，为 IBD 治疗带来了另一个重要的转折点。在过去的 10 年中，人们对这些方法在 IBD 研究中的兴趣日益浓厚，它们为 IBD 患者提供了更好的临床结果的有前途的途径。
由于数据量庞大，并且需要对数据进行手动解释，因此开发用于评估 IBD 和为临床管理提供信息的新工具具有挑战性。最近，机器和深度学习模型已被用于通过自动化对来自几种诊断方式的数据进行审查，从而以高精度来简化 IBD 的诊断和评估。这些方法减少了临床医生花费在手动审查数据以制定评估上的时间。
机器学习和深度学习在医学领域的兴趣日益浓厚，这些方法有望彻底改变我们治疗 IBD 的方式。在这里，我们重点介绍了使用这些技术评估 IBD 的最新进展，并讨论了利用这些技术来改善临床结果的方法。
