Artificial intelligence and frozen section histopathology: A systematic review

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.

Automated summary

Frozen sections are a useful tool in pathology, but their variable image quality might hinder the use of artificial intelligence and machine learning. This study identifies research on machine learning models trained or tested on frozen section images. Overall, convolutional neural networks perform the best, with physicians' involvement improving the model's performance. Models trained on frozen sections show good generalizability, while models trained on formalin-fixed tissue perform significantly worse.