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人工智能测量克罗恩病中的内性

Lingrui Cai¹, Emily Wittrup¹, Cristian Minoccheri¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association

|August 20, 2025

概括

通过分析结肠镜视频,计算机视觉准确地量化克罗恩病 (CD) 的活性. 与标准评分系统相比,这种自动化方法对更个性化的内镜评估有希望.

科学领域:

胃肠病学和医学成像
医学的人工智能
计算病理学

背景情况:

鉴定克罗恩病 (CD) 的内学活性是具有挑战性的,因为粘膜损伤不齐.
像克罗恩病简单内镜评分 (SES-CD) 这样的标准评分系统在量化疾病负担方面存在局限性.
计算机视觉内镜 (CVE) 评估为客观疾病量化提供了一个潜在的解决方案.

研究的目的:

评估计算机视觉内镜 (CVE) 在量化克罗恩病 (CD) 粘膜和损伤的性能.
将CVE测量的准确性和一致性与已建立的SES-CD进行比较.
确定CVE是否能够有效地区分临床缓解和活跃疾病状态.

主要方法:

使用CVE分析了第三阶段临床试验 (STARDUST,SEAVUE) 的结肠镜视频.
在专家注释上训练了一个细分模型,以识别和量化的区域,严重程度和大小.
对量化,局部化和与临床缓解相关性 (CDAI < 150) 的CVE衍生指标与SES- CD得分进行比较.

主要成果:

这种CVE细分模型的性能与人类注释器相比较 (DICE 0.591与0.462).
CVE测量与SES-CD得分有很强的相关性 (r=0.73-0.85),表明良好一致.
在分离临床缓解状态方面,CVE测量显示效果大于SES- CD (g=0. 416对g=0. 290).

结论:

计算机视觉内镜 (CVE) 评估提供了克罗恩病中和粘膜损伤的自动定量.
CVE与SES-CD的概念一致,并为内镜评估提供了增强的细节性.
这项技术有可能改善个人化疾病管理.

关键词:

克罗恩病人工智能计算机视觉疾病活动评估内镜评分

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