Mayo Clinic Alix School of Medicine, Rochester, Minnesota, USA.
Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, USA.
J Cutan Pathol. 2023 Sep;50(9):852-859. doi: 10.1111/cup.14481. Epub 2023 Jul 2.
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.
冰冻切片是一种有用的病理工具，但图像质量的变化可能会阻碍人工智能和机器学习在其解读中的应用。我们旨在确定目前在冰冻切片图像上进行训练或测试的机器学习模型的研究情况。我们在 PubMed 和 Web of Science 上搜索了在任何一年发表的介绍新机器学习模型的文章。有 18 篇论文完全符合纳入标准。所有论文都展示了至少一个在冰冻切片图像上训练或测试的新型模型。总的来说，卷积神经网络的性能往往最好。当医生能够查看模型的输出时，他们在测试任务中的表现往往优于模型或医生单独的表现。在其他玻片准备上测试时，在冰冻切片上训练的模型表现良好，但仅在福尔马林固定组织上训练的模型在其他模式下的表现明显较差。这不仅表明机器学习可以应用于冰冻切片图像处理，而且使用冰冻切片图像可能会增加模型的通用性。此外，人工智能与专家医生合作可能是冰冻切片组织病理学的未来。
