Poster
Validation of a Machine Learning-Based Approach (DELTA Liver Fibrosis Score) for the Assessment of Histologic Response in Patients with Advanced Fibrosis due to NASH
AASLD 2020
Study Background
♦Fibrosis, as assessed by liver biopsy, is the primary determinant of disease progression in patients with nonalcoholic steatohepatitis (NASH)
♦ Liver biopsy plays an integral role in the diagnosis and monitoring of disease progression, both in routine clinical practice and clinical trials
♦ Human pathologist staging of fibrosis is limited by 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
♦ Liver biopsy plays an integral role in the diagnosis and monitoring of disease progression, both in routine clinical practice and clinical trials
♦ Human pathologist staging of fibrosis is limited by 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
Authors
Taylor-Weiner et al