SynthesisJournal of advanced research2026
Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis.
Synthesis in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Development and validation of a risk model for non-fatty chronic liver disease in middle-aged and older adults with metabolic syndrome.BMC gastroenterology · 2026Article
- Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.Journal of clinical medicine · 2026Article
- Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Genetic modulators of metabolic dysfunction-associated steatotic liver disease (MASLD) and their epistatic interactions: from in vitro and animal models to clinical outcomes.BMC medical genomics · 2026Review
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- [Clinical research progress in 2025 for hepatic fibrosis, cirrhosis, and portal hypertension].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study.BMC gastroenterology · 2026Article
- Incorporating Insulin Resistance Biomarkers into Machine Learning Models Enhances Diagnostic Accuracy for Metabolic Dysfunction-Associated Steatotic Liver Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Metabolic dysfunction associated steatotic liver disease: mechanisms, diagnosis, and management in adults.BMJ medicine · 2026Review
- Review
- Machine learning-based hepatocellular carcinoma risk prediction model for patients with HBV-related compensated advanced chronic liver disease.Journal of cancer research and clinical oncology · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMetabolic dysfunction-associated steatotic liver disease (MASLD) can progress to metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis, contributing to a heavier global health burden. Non-invasive diagnostic tools developed using machine learning (ML) and deep learning (DL), two representative artificial intelligence algorithms, are increasingly being explored for MASH and its related fibrosis assessment.
objectivesThis study aimed to compare the diagnostic performance of different ML and DL models and identify the top-performing models for diagnosing MASH and associated liver fibrosis.
methodsA systematic review and meta-analysis were conducted across PubMed, Web of Science, Embase and Cochrane Library from inception to May 18, 2025. Pooled area under the receiver operator characteristic curve (AUROC) values with 95 % confidence interval (CI) were calculated. Accuracy, specificity, sensitivity, positive predictive values, and negative predictive values were also recorded.
resultsOf 4,314 studies initially identified, 106 met the inclusion criteria, with 35 studies (ML: n = 28; DL: n = 7) providing data for analysis. Logistic Regression and Neural Network are the most commonly algorithms applied in ML and DL, respectively. The pooled AUROCs for diagnosing MASH were 0.833 (95 %CI: 0.806-0.860) for ML models and 0.841 (95 %CI: 0.782-0.900) for DL models. Light Gradient Boosting Machine (LightGBM) and ResNet50 were the best-performing models for diagnosing MASH within ML and DL algorithms, respectively, achieving corresponding AUROCs of 0.920 (95 %CI: 0.916-0.924) and 0.960 (95 %CI: 0.951-0.969). For fibrosis diagnosis, ML models had a pooled AUROC of 0.826 (95 %CI: 0.792-0.860), with Categorical Boosting (CatBoost) achieving the highest AUROC of 0.960 (95 %CI: 0.950-0.970). DL models yielded the pooled AUROC of 0.875 (95 %CI: 0.816-0.934) for fibrosis diagnosis.
conclusionsBoth ML and DL models demonstrated strong diagnostic performance for MASH and liver fibrosis, with DL achieving marginally higher AUROCs. AI-driven approaches show promise in MASLD management.
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