Artificial intelligence and frozen section histopathology: A systematic review

Abstract

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.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment

“…However, cutting additional tissue sections could introduce further bottlenecks when using AI tools for intraoperative assessment. Yet, some prior research has suggested that AI might expedite the decision on when to section blocks 6,21,22 . The prevalence of low‐quality sections can differ among institution, thus influencing their impact.…”

“…Yet, some prior research has suggested that AI might expedite the decision on when to section blocks. 6,21,22 The prevalence of low-quality sections can differ among institution, thus influencing their impact. This variability warrants further investigation.…”

Principles, applications, and future of artificial intelligence in dermatology

“…Beyond identifying diagnoses via clinical images and electronic health record notes, machine learning techniques are being applied in dermatopathology ( 66 , 67 ). Groups have developed models to classify basal cell carcinoma in digitized Mohs micrographic surgery histology slides to reduce the workload of manually examining these slides ( 68 ).…”