Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease.
Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease.
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DOI：
10.1093/ibd/izaa211
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发表时间：
2020-09
影响因子：
4.9
通讯作者：
S. Syed;R. Stidham
中科院分区：
文献类型：
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作者：
S. Syed;R. Stidham
自动图像分析方法已显示出复制组织学和内窥镜图像的专家解释的潜力，这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）诊断、严重程度评估和治疗决策需要多模式专家数据解释和集成，这可以通过机器学习分析的应用得到显著帮助。本文介绍了用于成像分析的机器学习的基本概念，并重点介绍了IBD中自动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，组织学和内窥镜图像可以解释与知识专家相似的准确性。令人鼓舞的结果支持现有疾病活动性评分工具自动化的潜力，具有高重现性，速度和可访问性，从而提高IBD评估的标准化。尽管在临床实施之前必须解决围绕地面实况定义，技术障碍和广泛多中心评估需求的挑战，但自动化图像分析可能会改善标准化IBD评估的可及性，并推进疾病测量的基本概念。
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.