ReviewNature reviews. Gastroenterology & hepatology2024
Digital pathology for nonalcoholic steatohepatitis assessment.
Review in Nature reviews. Gastroenterology & hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 28 citations in OpenAlex.
- Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology.Liver international : official journal of the International Association for the Study of the Liver · 2024Trial
- Hypothyroidism and Metabolic Dysfunction-Associated Steatotic Liver Disease: Mechanisms, Clinical Links, and Therapeutic Implications.Current obesity reports · 2026Review
- AI-assisted fibrosis scoring in MASH: Exploring pathologist decision-making with an SHG-based AI digital pathology tool.JHEP reports : innovation in hepatology · 2026Article
- Digital pathology and lipid droplet size as a key determinant of discrepancies between histology and MRI gradings in steatotic liver disease.European radiology · 2026Article
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Panomics in metabolic dysfunction-associated steatotic liver disease: unravelling the drivers of disease heterogeneity.Clinical and molecular hepatology · 2026Review
- The pathophysiological role of portal hypertension in metabolic dysfunction-associated steatotic liver disease.Hepatology communications · 2025Review
- Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.The Journal of clinical investigation · 2025Review
- Extracellular Vesicles as Biomarkers in Chronic Hepatobiliary Diseases: An Overview of Their Interplay.International journal of molecular sciences · 2025Review
- Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence.World journal of gastroenterology · 2025Observational
- Three Neglected STARD Criteria Reduce the Uncertainty of the Liver Fibrosis Biomarker FibroTest-T2D in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis.Frontiers in medicine · 2025Article
- Hepatic Steatosis Analysis in Metabolic Dysfunction-Associated Steatotic Liver Disease Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Article
- A roadmap for clinical trials in MASH-related compensated cirrhosis.Nature reviews. Gastroenterology & hepatology · 2024Review
- AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response.Diagnostics (Basel, Switzerland) · 2024Article
- NAFLD (MASLD)/NASH (MASH): Does It Bother to Label at All? A Comprehensive Narrative Review.International journal of molecular sciences · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) - the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.
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What OpenQuestion holds
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.