ArticleScientific reports2019
Deep learning enables pathologist-like scoring of NASH models.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method.Journal of pathology informatics · 2026Article
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- 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
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis.Frontiers in medicine · 2025Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Detection of early-stage NASH using non-invasive hyperpolarizedScientific reports · 2024Article
- Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression.Clinical and molecular hepatology · 2024Article
- Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- Quantitative Evaluation by Digital Pathology of Immunohistochemical Expression of CK7, CK19, and EpCAM in Advanced Stages of NASH.Biomedicines · 2024Article
- Digital pathology for nonalcoholic steatohepatitis assessment.Nature reviews. Gastroenterology & hepatology · 2024Review
- Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence.Journal of clinical pathology · 2023Article
- Article
- Pharmacological Prevention of Ectopic Erythrophagocytosis by Cilostazol Mitigates Ferroptosis in NASH.International journal of molecular sciences · 2023Article
- Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat.Laboratory animal research · 2023Article
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.Clinical and molecular hepatology · 2023Review
- Artificial intelligence and deep learning: New tools for histopathological diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) and the progressive form of non-alcoholic steatohepatitis (NASH) are diseases of major importance with a high unmet medical need. Efficacy studies on novel compounds to treat NAFLD/NASH using disease models are frequently evaluated using established histological feature scores on ballooning, inflammation, steatosis and fibrosis. These features are assessed by a trained pathologist using microscopy and assigned discrete scores. We demonstrate how to automate these scores with convolutional neural networks (CNNs). Whole slide images of stained liver sections are analyzed using two different scales with four CNNs, each specialized for one of four histopathological features. A continuous value is obtained to quantify the extent of each feature, which can be used directly to provide a high resolution readout. In addition, the continuous values can be mapped to obtain the established discrete pathologist-like scores. The automated deep learning-based scores show good agreement with the trainer - a human pathologist.
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