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性结肠炎炎炎症活动水平的病理学家级诊断使用自动组织学分级方法

Chengfei Cai¹, Qianyun Shi², Jun Li³

¹School of Automation, Nanjing University of Information Science and Technology, Nanjing 21004, Jiangsu Province, China; Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 21004, Jiangsu Province, China; College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu Province, China.

International journal of medical informatics

|October 13, 2024

概括

这项研究开发了一种AI系统,用于对性结肠炎 (UC) 炎症活动进行分级,使用病理图像上的深度学习. 人工智能实现了与经验丰富的病理学家可比的性能,提高了诊断准确度.

科学领域:

病理学病理学病理学
人工智能的人工智能
胃肠病学胃肠病学

背景情况:

炎症性肠病 (IBD),包括性结肠炎 (UC),是一个日益严重的全球健康问题.
对UC炎症活动的精确组织学分级对于患者管理至关重要.
从病理图像来诊断IBD的计算方法仍未得到充分探索.

研究的目的:

建立人工智能辅助的诊断系统,用于性结肠炎 (UC) 炎症活动的组织学分级.
开发和验证深度学习模型,用于分析UC病理的全幻灯片图像 (WSIs).

主要方法:

开发了一个使用ResNet50的深度学习 (DL) 模型和一个多实例学习 (MIL) 方法,并关注自我.
该模型在南京鼓楼医院 (内部) 的603个UC WSIs和珠江医院 (外部) 的212个UC WSIs上进行了训练和验证.
该系统汇总了图像补丁功能,以代表整个WSIs来预测炎症活动水平.

主要成果:

人工智能系统在区分炎症活动的存在/不存在方面表现出高准确性,AUC为0.863 (内部) 和0.947 (外部).
为了分类不同活动水平,平均宏观AUC为0.827 (内部) 和0.908 (外部),微观AUC为0.816 (内部) 和0.898 (外部).
该模型在内部和外部测试集中实现了超过0.80的灵敏度和特异性值.

结论:

开发的AI算法实现了与具有5年经验的病理学家可比的诊断能力.
人工智能系统在评分UC炎症活动方面超过了其他现有的MIL算法.
这种人工智能辅助系统有望提高UC组织学分级的准确性和效率.

关键词:

炎症性活动水平.多实例学习是指多实例的学习.病理学家水平的诊断诊断病理学家水平的诊断.性结肠炎是一种整个幻灯片图像的图像.

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