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深度学习有助于区分自身免疫性肝炎和原发性胆道胆道炎

Alessio Gerussi^1,2, Oliver Lester Saldanha^3,4, Giorgio Cazzaniga⁵

¹Division of Gastroenterology, Center for Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.

JHEP reports : innovation in hepatology

|January 20, 2025

概括

一种新的深度学习模型,即自身免疫性肝脏神经估计器 (ALNE),使用肝脏活检幻灯片,准确地区分自身免疫性肝炎 (AIH) 与原发性胆道胆炎 (PBC). 这种人工智能工具有助于差异诊断,减少潜在的治疗错误.

科学领域:

肝病学和数字病理学.
人工智能在医学诊断中的应用.

背景情况:

自体免疫性肝炎 (AIH) 和初级胆道胆道炎 (PBC) 经常出现重叠的胆道和接口肝炎特征.
错误的诊断可能导致错误的治疗和不良的患者结果.
准确的差异诊断对于有效的患者管理至关重要.

研究的目的:

调查深度学习 (DL) 管道对AIH和PBC的差异诊断的有效性.
开发和验证DL模型,用于分析数字化肝活检幻灯片.
通过人工智能辅助病理学来减少诊断错误和改善患者护理.

主要方法:

一项多中心研究利用了来自六个欧洲中心的数字化肝脏活检幻灯片库.
一种新的DL模型,即自身免疫性肝脏神经估计器 (ALNE),在没有人类注释的全幻灯片图像 (WSIs) 上进行了训练.
ALNE模型在外部数据集上得到验证,并与临床病理诊断和观察者间变异性进行了比较.

主要成果:

在外部验证中,ALNE模型在接收器操作特征曲线下的面积达到0.81,用于区分AIH和PBC.
注意热图表明ALNE专注于炎症区域,将它们与AIH联系起来.
该模型的表现突出了与一般病理学家评估的差异,显示了显著的观察者间变异性 (弗莱斯的卡帕=0.09).

结论:

ALNE模型代表了AIH和PBC之间的定量和准确差异诊断的开创性系统.
这种DL方法在使用数字化组织学来区分这些肝脏疾病方面显示出显著的潜力.
这些发现支持在病理学中使用人工智能来提高诊断准确性和一致性.

关键词:

人工智能的人工智能是人工智能.这是一种自身免疫力.计算病理学计算病理学数字病理学数字病理学肝脏肝脏肝脏肝脏罕见的肝脏疾病罕见的肝脏疾病

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