Commentary
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.
A. Taylor-Weiner et al. (PathAI, Boston, Massachusetts, USA) describe here a machine learning-based approach to liver histology assessment, which accurately characterises disease severity and heterogeneity, and sensitively quantifies treatment response in NASH.
The machine learning-based predictions show strong correlations with expert pathologists and are prognostic of progression to cirrhosis and liver-related clinical events.
The authors also developed a new heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment (DELTA) Liver Fibrosis score.
