---
title: Machine Learning Fibrosis Models Based on Liver Histology Images Accurately Characterize the Heterogeneity of Cirrhosis Due to Nonalcoholic Steatohepatitis
description: Machine Learning Fibrosis Models Based on Liver Histology Images Accurately Characterize the Heterogeneity of Cirrhosis Due to Nonalcoholic Steatohepatitis
---

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# [Machine Learning Fibrosis Models Based on Liver Histology Images Accurately Characterize the Heterogeneity of Cirrhosis Due to Nonalcoholic Steatohepatitis](https://www.pathai.com/resources/machine-learning-fibrosis-models-based-on-liver-histology-images-accurately-characterize-the-heterogeneity-of-cirrhosis-due-to-nonalcoholic-steatohepatitis)

 Written by [Admin](https://www.pathai.com/resources/author/admin) | Nov 8, 2019 5:00:00 AM

## Study Background

- Nonalcoholic steatohepatitis (NASH) cirrhosis is characterized by heterogeneity in histology, clinical presentation, and prognosis1
- In patients with NASH, the presence of cirrhosis is associated with an increased risk of liver-related and all-cause mortality, but identification of those at risk for liver-related clinical events may be challenging2-4
- Although machine learning (ML) approaches have been used to evaluate liver histology in NASH,5,6 the utility of these approaches to characterize fibrosis and risk stratify patients with cirrhosis requires evaluation

[View Poster](https://www.pathai.com/hubfs/2025%20Assets/Copy-of-Younossi-2-AASLD-2019-68x44@100-printready.pdf)

[View full post](https://www.pathai.com/resources/machine-learning-fibrosis-models-based-on-liver-histology-images-accurately-characterize-the-heterogeneity-of-cirrhosis-due-to-nonalcoholic-steatohepatitis)

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