Abstract：
Crohn\'s disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of surgical resection specimens to distinguish CD from ITB. Overall, 1973 WSI from 85 cases from 3 centers were obtained. The DL model was established in internal training and validated in external test cohort, evaluated by area under receiver operator characteristic curve (AUC). Diagnostic results of pathologists were compared with those of the DL model using DeLong\'s test. DL model had case level AUC of 0.886, 0.893 and slide level AUC of 0.954, 0.827 in training and test cohorts. Attention maps highlighted discriminative areas and top 10 features were extracted from CD and ITB. DL model\'s diagnostic efficiency (AUC = 0.886) was better than junior pathologists (*1 AUC = 0.701, P = 0.088; *2 AUC = 0.861, P = 0.788) and inferior to senior GI pathologists (*3 AUC = 0.910, P = 0.800; *4 AUC = 0.946, P = 0.507) in training cohort. In the test cohort, model (AUC = 0.893) outperformed senior non-GI pathologists (*5 AUC = 0.782, P = 0.327; *6 AUC = 0.821, P = 0.516). We developed a DL model for the classification of CD and ITB, improving pathological diagnosis accuracy effectively.
摘要：
克罗恩病（CD）和肠结核（ITB）具有相似的组织病理学特征，鉴别诊断可能是病理学家的两难选择。本研究旨在应用深度学习（DL）分析手术切除标本的整个幻灯片图像（WSI），以区分CD和ITB。总的来说,1973年WSI从3个中心的85例病例中获得。在内部训练中建立DL模型，并在外部测试队列中进行验证。通过受试者操作特征曲线下面积(AUC)评估。使用DeLong检验将病理学家的诊断结果与DL模型的诊断结果进行比较。DL模型在训练和测试队列中的病例水平AUC为0.886、0.893，幻灯片水平AUC为0.954、0.827。注意图突出了区分区域，并从CD和ITB中提取了前10个特征。DL模型的诊断效率（AUC=0.886）优于初级病理学家（*1AUC=0.701，P=0.088；*2AUC=0.861，P=0.788），低于高级GI病理学家（*3AUC=0.910，P=0.800；*4AUC=0.946，P=0.507）。在测试队列中，模型(AUC=0.893)优于高级非GI病理学家(*5AUC=0.782,P=0.327;*6AUC=0.821,P=0.516).我们开发了一个用于CD和ITB分类的DL模型，有效提高病理诊断的准确性。
