ArticleAnnals of diagnostic pathology2020
Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.
Article in Annals of diagnostic pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- The Elucidating Pathways of Steatohepatitis (EPoS) staging system: A reproducible 7-tiered fibrosis scoring system in MASLD.JHEP reports : innovation in hepatology · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Review
- From mechanisms to management: Early detection and improved treatment of MASLD and its related hepatocellular carcinoma.Hepatology communications · 2025Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Effect of liver biopsy size on MASLD fibrosis assessment by second-harmonic generation/two-photon excitation fluorescence microscopy.JHEP reports : innovation in hepatology · 2025Article
- Assessing the diagnostic accuracy of ChatGPT-4 in the histopathological evaluation of liver fibrosis in MASH.Hepatology communications · 2025Article
- Radiologic correlation with fatty liver and adrenal adenoma using dual echo chemical shift magnetic resonance imaging.Abdominal radiology (New York) · 2025Article
- Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care.Cancers · 2025Review
- Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis.Frontiers in medicine · 2025Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults.Journal of pathology informatics · 2025Article
- Shear Wave Dispersion Elastography in ALD and MASLD: Comparative Pathophysiology and Clinical Potential-A Narrative Review.Journal of clinical medicine · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions.Journal of hepatology · 2024Review
- Recompensation in cirrhosis: unravelling the evolving natural history of nonalcoholic fatty liver disease.Nature reviews. Gastroenterology & hepatology · 2024Review
- Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.Clinics and practice · 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
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integrated artificial intelligence-based automated tool to detect and quantify hepatic fibrosis and assess its architectural pattern in NAFLD liver biopsies. Digital images of the trichrome-stained slides of liver biopsies from patients with NAFLD and different severity of fibrosis were used. Two expert liver pathologists semi-quantitatively assessed the severity of fibrosis in these biopsies and using a web applet provided a total of 987 annotations of different fibrosis types for developing, training and testing supervised machine learning models to detect fibrosis. The collagen proportionate area (CPA) was measured and correlated with each of the pathologists semi-quantitative fibrosis scores. Models were created and tested to detect each of six potential fibrosis patterns. There was good to excellent correlation between CPA and the pathologist score of fibrosis stage. The coefficient of determination (R
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Registered trials
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.