我们训练了一个人工智能（AI）算法来识别基底细胞癌（BCC）并区分BCC和组织形态学模拟物。总共收集了1061个玻片：616个含有BCC和445个不含有BCC。BCC玻片是前瞻性收集的，反映了在常规病理学实践中遇到的标本类型和形态学变化的范围。前瞻性和回顾性选择了BCC的良性和恶性组织形态学模拟物，包括病理学家认为具有诊断挑战性的病例。玻片被数字化扫描以创建整个切片图像（WSI），该图像被分成表示65,535微米²组织区域的小块。病理学家注释了数据，产生了87205个标记为存在BCC的小块和1688697个标记为不存在BCC的小块。基于卷积神经网络的COntext-aware Multi-scale tool for Pathologists Assessing SlideS（COMPASS）模型被训练以提供小面积和切片水平上BCC存在的概率。测试集包括246个玻片，其中147个含有BCC。 COMPASS AI模型表现出高准确性，将WSIs分类为含有BCC的灵敏度为98.0％，特异度为97.0％，代表了240个正确分类的WSIs，3个误诊病例和3个漏诊病例。通过使用BCC作为概念验证，我们展示了AI如何考虑实体内的形态变化，并准确地区别于组织形态学相似的实体。我们的研究突显了AI在常规病理学实践中的潜力。版权所有©2022澳大利亚皇家病理学院。由Elsevier B.V.出版。保留所有权利。

We trained an artificial intelligence (AI) algorithm to identify basal cell carcinoma (BCC), and to distinguish BCC from histological mimics. A total of 1061 glass slides were collected: 616 containing BCC and 445 without BCC. BCC slides were collected prospectively, reflecting the range of specimen types and morphological variety encountered in routine pathology practice. Benign and malignant histological mimics of BCC were selected prospectively and retrospectively, including cases considered diagnostically challenging for pathologists. Glass slides were digitally scanned to create a whole slide image (WSI), which was divided into patches representing a tissue area of 65,535 μm2. Pathologists annotated the data, yielding 87,205 patches labelled BCC present and 1,688,697 patches labelled BCC absent. The COMPASS model (COntext-aware Multi-scale tool for Pathologists Assessing SlideS) based on Convolutional Neural Networks, was trained to provide a probability of BCC being present at the patch level and the slide level. The test set comprised 246 slides, 147 of which contained BCC. The COMPASS AI model demonstrated high accuracy, classifying WSIs as containing BCC with a sensitivity of 98.0% and a specificity of 97.0%, representing 240 WSIs classified correctly, three false positives, and three false negatives. Using BCC as a proof of concept, we demonstrate how AI can account for morphological variation within an entity, and accurately distinguish from histologically similar entities. Our study highlights the potential for AI in routine pathology practice.Copyright © 2022 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.