Study Background
♦ Fibrosis is the primary determinant of disease progressionin patients with nonalcoholic steatohepatitis (NASH), but the
prognostic value of other histologic features is unclear
♦ Human pathologist staging of fibrosis and NAFLD Activity
Score (NAS) are limited by sampling variability, and intra- and
inter-reader variability
♦ Machine learning (ML) approaches to interpretation of liver
histology may enable more reliable and quantitative assessment
of both traditional and novel histologic features, with potential
prognostic relevance in NASH Pokkalla et al.
