Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

ABSTRACT:

Background

Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies.

Methods

We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequ

SUBMITTER: Sun L 

PROVIDER: S-EPMC7522765 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature