ReviewComputational and structural biotechnology journal2023
Artificial intelligence and deep learning: New tools for histopathological diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- From Molecular Mechanisms to Clinical Strategies: A Comprehensive Overview of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).International journal of molecular sciences · 2026Review
- Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.Physical and engineering sciences in medicine · 2026Article
- Therapeutic potential of TMSC-Exo for non-alcoholic fatty liver disease using the liver-on-a-chip model.Journal of translational internal medicine · 2026Article
- Testosterone deficiency synergistically exacerbates fructose-induced hepatic steatosis through gut microbiota and pyruvate in mice.American journal of physiology. Endocrinology and metabolism · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Roles of short-chain fatty acids in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis.World journal of hepatology · 2025Review
- AI in steatohepatitis diagnostics: precision beyond the microscope.Annals of medicine and surgery (2012) · 2025Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Establishing predictive models for malignant and inflammatory pulmonary nodules using clinical data and CT imaging features.Quantitative imaging in medicine and surgery · 2025Article
- Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care.Cancers · 2025Review
- Extracellular vesicle-mediated approaches for the diagnosis and therapy of MASLD: current advances and future prospective.Lipids in health and disease · 2025Review
- PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue.Computational and structural biotechnology journal · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Modernizing metabolic dysfunction-associated steatotic liver disease diagnostics: the progressive shift from liver biopsy to noninvasive techniques.Therapeutic advances in gastroenterology · 2024Review
- Diagnostic Value of Hepatic Mast Cell Concentration (MCC) in NAFLD and NASH Severity and Fibrosis Grade.Iranian journal of pathology · 2024Article
- Protocol of quantitative ultrasound techniques for noninvasive assessing of hepatic steatosis after bariatric surgery.Frontiers in surgery · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) is associated with metabolic syndrome and is rapidly increasing globally with the increased prevalence of obesity. Although noninvasive diagnosis of NAFLD/NASH has progressed, pathological evaluation of liver biopsy specimens remains the gold standard for diagnosing NAFLD/NASH. However, the pathological diagnosis of NAFLD/NASH relies on the subjective judgment of the pathologist, resulting in non-negligible interobserver variations. Artificial intelligence (AI) is an emerging tool in pathology to assist diagnoses with high objectivity and accuracy. An increasing number of studies have reported the usefulness of AI in the pathological diagnosis of NAFLD/NASH, and our group has already used it in animal experiments. In this minireview, we first outline the histopathological characteristics of NAFLD/NASH and the basics of AI. Subsequently, we introduce previous research on AI-based pathological diagnosis of NAFLD/NASH.
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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.