冰冻切片程序
章节(排版)
概化理论
人工智能
组织病理学
计算机科学
机器学习
卷积神经网络
医学物理学
人工神经网络
病理
医学
心理学
发展心理学
操作系统
作者
Benjamin G. Gorman,Mark A Lifson,Nahid Y. Vidal
摘要
Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin-fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology.
科研通智能强力驱动
Strongly Powered by AbleSci AI