A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

ABSTRACT:

Background and aims

Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response.

Approach and results

Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis,

SUBMITTER: Taylor-Weiner A 

PROVIDER: S-EPMC8361999 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature