Gu Phillip, Mendonca Oreen, Carter Dan, Dube Shishir, Wang Paul, Huang Xiuzhen, Li Debiao, Moore Jason H, McGovern Dermot P B
F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
University of Toronto, Toronto, ON, Canada.
Inflamm Bowel Dis. 2024 Dec 5;30(12):2467-2485. doi: 10.1093/ibd/izae030.
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
内镜检查、组织学检查和横断面成像，是炎症性肠病（IBD）检测、监测和预后评估的重要支柱。然而，这些检查的解读往往依赖于主观的人为判断，这可能导致诊断延迟、观察者内部和观察者之间的差异，以及潜在的诊断差异。随着全球IBD发病率的上升，以及这些数据的指数级数字化，对创新方法以简化诊断并提升临床决策的需求日益增长。在此背景下，人工智能（AI）技术应运而生，成为应对IBD不断演变挑战的适时解决方案。早期使用深度学习和放射组学方法进行IBD内镜检查、组织学检查和成像的研究，已显示出人工智能在检测、诊断、表征、分型和预测IBD方面具有可观的成果。尽管如此，现有文献存在固有局限性和知识空白，在人工智能能够转变为IBD的主流临床工具之前，这些问题需要得到解决。为了更好地理解将人工智能整合到IBD中的潜在价值，我们回顾现有文献，总结当前的认识，并找出知识空白，为未来的研究提供参考。
