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Manual histologic assessment is currently the accepted standard for diagnosing and monitoring disease progression in nonalcoholic steatohepatitis (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. Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensit
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