SynthesisBMC gastroenterology2021
Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis.
Synthesis in BMC gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
What it found
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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.
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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
47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2024Pooled it
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Development and validation of a machine learning-based model for identifying liver fibrosis in individuals with prior Schistosoma japonicum infection: a step toward precision management.Infectious diseases of poverty · 2026Article
- Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review.Health science reports · 2026Review
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Development and evaluation of machine learning models for predicting significant liver fibrosis stages: A retrospective analysis.Canadian liver journal · 2026Article
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Redox Modulation in Hepatic Fibrosis: Translating NOX1/4 Inhibition to Therapy.International journal of molecular sciences · 2025Review
- LivSCP: Improving Liver Fibrosis Classification Through Supervised Contrastive Pretraining.Diagnostics (Basel, Switzerland) · 2025Article
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Genetic predeterminants and recent advancements in steatotic liver disease: A roadmap toward precision hepatology.World journal of hepatology · 2025Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 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
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.Clinical transplantation · 2025Article
- Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.BMC infectious diseases · 2025Article
- Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care.Cancers · 2025Review
- The Role of Machine Learning Models in Predicting Cirrhosis Mortality: A Systematic Review.Cureus · 2025Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance.
methodsWe systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95% CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities, population and AI classifiers were performed.
resultsWe included 19 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography, magnetic resonance imaging and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.71-0.85), 0.89 (0.81-0.94), 0.72 (0.58-0.83), 0.92 (0.88-0.94) and 31.58 (11.84-84.25), respectively, for cirrhosis; 0.86 (0.80-0.90), 0.87 (0.80-0.92), 0.85 (0.75-0.91), 0.88 (0.82-0.92) and 37.79 (16.01-89.19), respectively; for advanced fibrosis; and 0.86 (0.78-0.92), 0.81 (0.77-0.84), 0.88 (0.80-0.93), 0.77 (0.58-0.89) and 26.79 (14.47-49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76-1.00), 0.91 (0.78-0.97), 0.95 (0.87-0.98), 0.93 (0.80-0.98) and 191.52 (38.82-944.81), respectively, for the diagnosis of liver steatosis.
conclusionsAI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice.
trial registrationThe protocol was registered with PROSPERO (CRD42020183295).
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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.