细胞形态学因处理技术不同而有所差别，这些差异对于使用深度学习进行自动化诊断构成了问题。我们研究了人工智能（AI）进行细胞检测或分类与AutoSmear（Sakura Finetek Japan）和液基细胞学（LBC）处理技术之间尚未阐明的关系。我们使用“You Only Look Once”（YOLO）版本5x算法，针对肺癌（LC）、宫颈癌（CC）、恶性胸膜间皮瘤（MM）和食管癌（EC）这四种细胞系，对AutoSmear和LBC制备进行了训练。使用检测和分类率来评估细胞检测的准确性。当训练和检测中使用相同处理技术的制备来进行1细胞（1C）模型时，AutoSmear模型的检测率比LBC模型高。当使用不同的处理技术进行训练和检测时，LC和CC的检测率在4细胞（4C）模型中显著低于1C模型，而MM和EC的检测率在4C模型中约低10%。在基于AI的细胞检测和分类中，应注意那些形态学因处理技术而显著改变的细胞，并建立相应的训练模型。©作者（们）2023年。由牛津大学出版社代表美国临床病理学会出版。保留所有权利。有关授权，请发送电子邮件至：journals.permissions@oup.com。

Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques.The "You Only Look Once" (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC). Detection and classification rates were used to evaluate the accuracy of cell detection.When preparations of the same processing technique were used for training and detection in the 1-cell (1C) model, the AutoSmear model had a higher detection rate than the LBC model. When different processing techniques were used for training and detection, detection rates of LC and CC were significantly lower in the 4-cell (4C) model than in the 1C model, and those of MM and EC were approximately 10% lower in the 4C model.In AI-based cell detection and classification, attention should be paid to cells whose morphologies change significantly depending on the processing technique, further suggesting the creation of a training model.© The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.