Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Inflamm Bowel Dis. 2020 Sep 18;26(10):1490-1497. doi: 10.1093/ibd/izaa211.
Automated image analysis methods have shown potential for replicating expert interpretation of histology and endoscopy images, which traditionally require highly specialized and experienced reviewers. Inflammatory bowel disease (IBD) diagnosis, severity assessment, and treatment decision-making require multimodal expert data interpretation and integration, which could be significantly aided by applications of machine learning analyses. This review introduces fundamental concepts of machine learning for imaging analysis and highlights research and development of automated histology and endoscopy interpretation in IBD. Proof-of-concept studies strongly suggest that histologic and endoscopic images can be interpreted with similar accuracy as knowledge experts. Encouraging results support the potential of automating existing disease activity scoring instruments with high reproducibility, speed, and accessibility, therefore improving the standardization of IBD assessment. Though challenges surrounding ground truth definitions, technical barriers, and the need for extensive multicenter evaluation must be resolved before clinical implementation, automated image analysis is likely to both improve access to standardized IBD assessment and advance the fundamental concepts of how disease is measured.
自动化图像分析方法已经显示出复制组织学和内窥镜图像专家解释的潜力，而这些图像传统上需要高度专业化和经验丰富的审查员。炎症性肠病（IBD）的诊断、严重程度评估和治疗决策需要多模式的专家数据解释和整合，机器学习分析的应用可以极大地帮助这一过程。本文介绍了用于成像分析的机器学习基本概念，并强调了 IBD 中自动化组织学和内窥镜解释的研究和开发。概念验证研究强烈表明，可以与知识专家一样准确地解释组织学和内窥镜图像。令人鼓舞的结果支持了利用高度可重复性、速度和可及性自动化现有疾病活动评分工具的潜力，从而提高 IBD 评估的标准化。尽管在临床实施之前必须解决真实定义、技术障碍以及对广泛多中心评估的需求等挑战，但自动化图像分析很可能既可以改善标准化 IBD 评估的获取途径，又可以推进疾病测量的基本概念。
