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IBDAIM:用于分析肠道活检病理图像的人工智能,用于辅助炎症性肠病综合诊断

Chengfei Cai¹, Qianyun Shi², Mingxin Liu³

¹College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu Province, PR China; Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu Province, PR China.

International journal of medical informatics

|June 28, 2025

概括

一个人工智能 (AI) 模型,IBDAIM,有助于从病理图像中诊断炎症性肠病 (IBD). 这种人工智能工具显著提高了病理学家分析肠道活检的诊断准确性和效率.

科学领域:

计算病理学计算病理学
医学中的人工智能.
胃肠病学和肝病学胃肠学和肝病学

背景情况:

诊断炎症性肠病 (IBD),包括克罗恩病 (CD) 和性结肠炎 (UC),由于病理图像中的复杂组织学特征,提出了挑战.
肠道活检的全幻灯片图像 (WSIs) 需要专家分析才能准确诊断IBD.

研究的目的:

开发和验证人工智能 (AI) 模型IBDAIM,以协助病理学家快速准确地诊断IBD.
评估IBDAIM模型在使用WSIs区分正常肠道粘膜,CD和UC的性能.

主要方法:

一项回顾性队列研究,利用来自两个机构 (南京鼓楼医院和珠江医院) 的数据.
开发一个弱监督的深度学习模型 (IBDAIM),利用WSI级的诊断标签.
整合补丁级特征,使用补丁概率直方图 (PLH) 和词包 (BoW) 进行WSI级别表示.
使用AUROC的性能评估,准确性,灵敏性和特异性,并进行外部验证.

主要成果:

在区分正常的粘膜和IBD方面,IBDAIM模型取得了很高的性能 (内部测试AUROC:0.998,外部测试AUROC:0.967).
为了区分CD和UC,该模型表现出强的结果 (内部测试AUROC:0.972,外部测试AUROC:0.952).
人工智能模型的表现超过了五位病理学家的表现,人工智能协助显著提高了整体诊断准确度.

结论:

IBDAIM模型在通过病理图像诊断IBD方面表现出很高的有效性.
这种AI工具有效地帮助病理学家识别正常,CD和UC组织,提高诊断效率和准确性.
IBDAIM模型支持改善IBD管理中的临床决策和患者结果.

关键词:

人工智能的人工智能是人工智能.协助诊断帮助诊断活检病理图像活检病理图像深度学习是一种深度学习.炎症性肠病是一种炎症性肠病.

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