@Article{信息:doi/10.2196/23436，作者=“施密特，马克斯和马伦，罗曼·克里斯托弗和赫克勒，阿希姆和斯坦辛格，阿尔布雷希特和豪斯查尔，阿克塞尔和韦肯塔尔，迈克尔和蒂曼，马库斯和克拉尔，迪特尔和库茨纳，海因茨和乌蒂卡尔，约亨·斯文和哈夫坎普，塞巴斯蒂安和凯瑟，雅各布·尼古拉斯和克劳斯琴，弗雷德里克和克里霍夫-亨宁，伊娃和弗{\“o}林，斯特凡和冯·卡莱，克里斯托弗和布林克，提图斯·约瑟夫”，题目=“深度学习数字病理学中的隐变量及其可能导致批处理的影响:预测模型研究”，期刊=“J Med Internet Res”，年=“2021”，月=“Feb”，日=“2”，卷=“23”，数=“2”，页=“e23436”，关键词=“人工智能”;机器学习;深度学习;神经网络;卷积神经网络;病理学;临床病理学;数字病理;陷阱; artifacts", abstract="Background: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. Objective: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. Methods: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95{\%} confidence interval of its mean balanced accuracy was above 50.0{\%}. Results: A mean balanced accuracy above 50.0{\%} was achieved for all four tasks, even when considering the lower bound of the 95{\%} confidence interval. Performance between tasks showed wide variation, ranging from 56.1{\%} (slide preparation date) to 100{\%} (slide origin). Conclusions: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification. ", issn="1438-8871", doi="10.2196/23436", url="//www.mybigtv.com/2021/2/e23436", url="https://doi.org/10.2196/23436", url="http://www.ncbi.nlm.nih.gov/pubmed/33528370" }