ReviewDiagnostics (Basel, Switzerland)2021
Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.Abdominal radiology (New York) · 2026Pooled it
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Expanding the Horizon: A Roadmap for Artificial Intelligence Integration in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- Gut Microbiota Changes in Metabolic Dysfunction-Associated Steatohepatitis and Inflammatory Bowel Disease: Common Pathogenic Features.Current issues in molecular biology · 2025Review
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
- Hidden in the Fat: Unpacking the Metabolic Tango Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Metabolic Syndrome.International journal of molecular sciences · 2025Review
- Clinical Phenotypes May be Able to Identify Populations With Nonalcoholic Fatty Liver-Spectrum Disease.Gastro hep advances · 2025Article
- ANN uncertainty estimates in assessing fatty liver content from ultrasound data.Computational and structural biotechnology journal · 2024Article
- Virtual tissue microstructure reconstruction across species using generative deep learning.PloS one · 2024Article
- Digital pathology for nonalcoholic steatohepatitis assessment.Nature reviews. Gastroenterology & hepatology · 2024Review
- Non-alcoholic fatty liver disease: the pathologist's perspective.Clinical and molecular hepatology · 2023Review
- Management of metabolic-associated fatty liver disease: The diabetology perspective.World journal of gastroenterology · 2023Review
- Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.Bioengineering (Basel, Switzerland) · 2022Review
- Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.Scientific reports · 2022Article
- Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis.Therapeutic advances in gastroenterology · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging.
methodsPubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records).
resultsOur search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software.
conclusionsWe found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.
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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.