ArticleHepatology (Baltimore, Md.)2021
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.
Article in Hepatology (Baltimore, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed, 193 citations in OpenAlex.
- Pharmacokinetics and Safety of Fenofibrate in Participants with Mild Hepatic Impairment or with Advanced Fibrosis due to Metabolic-Associated Fatty Liver Disease.Journal of clinical pharmacology · 2025Trial
- Semaglutide 2·4 mg once weekly in patients with non-alcoholic steatohepatitis-related cirrhosis: a randomised, placebo-controlled phase 2 trial.The lancet. Gastroenterology & hepatology · 2023Trial
- Liver Biopsy in Metabolic Dysfunction-Associated Steatohepatitis Clinical Trials: Potential Pitfalls and Solutions.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- AI digital pathology as a key tool providing in-depth understanding of the progression and regression of MASH and fibrosis in male mouse models.Nature communications · 2026Article
- The Elucidating Pathways of Steatohepatitis (EPoS) staging system: A reproducible 7-tiered fibrosis scoring system in MASLD.JHEP reports : innovation in hepatology · 2026Article
- Evaluation methods of hepatic steatosis: From conventional techniques to emerging biomarkers.World journal of hepatology · 2026Review
- Clinical biomarkers and biosensors for early detection of metabolic dysfunction-associated steatohepatitis (MASH): a translational perspective.Frontiers in bioengineering and biotechnology · 2026Review
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- Quantitative Approaches to Accelerate MASH Drug Discovery and Development.Clinical pharmacology and therapeutics · 2025Review
- Article
- From mechanisms to management: Early detection and improved treatment of MASLD and its related hepatocellular carcinoma.Hepatology communications · 2025Review
- Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.Hepatology (Baltimore, Md.) · 2025Review
- [Current research progress status in identifying hepatocellular ballooning degeneration in nonalcoholic steatohepatitis].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- A prediction model for genetic cholestatic disease in infancy using the machine learning approach.Journal of pediatric gastroenterology and nutrition · 2025Article
- Diagnostic Applications of Artificial Intelligence in Liver Diseases.Journal of clinical medicine · 2025Review
- Review
- Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.Scientific reports · 2025Article
- Evaluating the Discriminative Performance of Noninvasive Biomarkers in Chronic Hepatitis B/C, Alcoholic Cirrhosis, and Nonalcoholic Cirrhosis: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Assessing the diagnostic accuracy of ChatGPT-4 in the histopathological evaluation of liver fibrosis in MASH.Hepatology communications · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
31 authors at 14 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsManual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. APPROACH AND
resultsHere, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression.
conclusionsOur ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.