A: Masson’s Trichrome-stained liver tissue with a masked portal region. B: H&E-stained liver tissue with a masked ballooning cell.
Summary: Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease. It results in an accumulation of fat in the liver (steatosis), and can progress to a more pathologically significant form of NAFLD known as non-alcoholic steatohepatitis (NASH). Patients or rodent models of NAFLD and NASH present on a spectrum of the disease, characterized by hepatitis (inflammation) and hepatocellular ballooning (cellular injury), which can lead to excessive fibrosis and scarring.
Currently, diagnosis is confirmed by liver histology that is qualitatively analyzed by experienced pathologists who assign scores for each feature. However, documented inter-pathologist variability in scoring and the semi-quantitative nature of the scoring system itself highlight the need for new quantitative methods to ensure the unbiased, consistent assessment of disease.
imageDx™: NASH is a collection of artificial intelligence (AI)-based pathology models to provide quantitative histopathology data from liver tissue. These machine learning algorithms were developed with input from experienced veterinary pathologists with the goal of providing a more quantitative and reproducible analysis of the tissue pathology. The data outputs are listed below.
Mean Vesicle Size
Mean Lipid per Hepatocyte
Percentage (%), Area (mm2)
Ballooning Hepatocyte Density
Immune Cell Foci
Count, Area (mm2), Density (cells/mm2)
Foci count, Mean foci size
Present or Absent
Tissue Area Analyzed
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