Non-alcoholic steatohepatitis (NASH) biopsies are evaluated using both hematoxylin and eosin (H&E) and Masson’s Trichrome (TC) stains, but this process is subject to high variability. Machine learning (ML) methods train distinct H&E and TC models to assess respective histologic components and improve reproducibility over manual scoring. We previously developed a novel ML-based model to extract and combine complementary histologic information from H&E and TC stains to predict NASH Clinical Research Network (CRN) grades/stages, improving accuracy over single-stain ML approaches. Here, we performed exploratory analyses of learned model-derived features.
Abel et al.
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