ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Subtyping metabolic dysfunction-associated steatotic liver disease using electronic health record-linked genomic cohorts reveals diverse etiologies and progression.Nature communications · 2026Article
- Article
- Harnessing AI-driven approaches for detecting metabolic dysfunction-associated steatotic liver disease, assessing fibrosis, and stratifying hepatocellular carcinoma risk: a scoping review.Frontiers in oncology · 2026Review
- 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
- Independent Predictors of Major Adverse Cardiac and Hepatic Outcomes Following Acute Liver Complications of Pregnancy; A Retrospective Cohort Study.Archives of academic emergency medicine · 2026Article
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Comment on "Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease".Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- GLM7 - A Novel Composite Glycolipid Index Derived from Routine Health Indicators for Enhanced Diagnosis and Prediction of Multimorbidity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Development of a machine learning model for hepatic steatosis screening using non-invasive Traditional Chinese Medicine diagnostics and clinical variables: a health checkup study with community screening potential.Frontiers in medicine · 2025Article
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15 authors.
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Abstract
Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease and is considered the hepatic manifestation of metabolic syndrome, triggering out adverse outcomes. A stacked multimodal machine learning model is constructed and validated for early identification and prognosis stratification of NAFLD by integrating genetic and clinical data sourced from 36 490 UK Biobank and 9 007 Nanfang Hospital participants and extracted its probabilities as in-silico scores for NAFLD (ISNLD). The efficacy of ISNLD is evaluated for the early prediction of severe liver disease (SeLD) and analyzed its association with metabolism-related outcomes. The multimodal model performs satisfactorily in classifying individuals into low- and high-risk groups for NAFLD, achieving area under curves (AUCs) of 0.843, 0.840, and 0.872 within training, internal, and external test sets, respectively. Among high-risk group, ISNLD is significantly associated with intrahepatic and metabolism-related complications after lifestyle factors adjustment. Further, ISNLD demonstrates notable capability for early prediction of SeLD and further stratifies high-risk subjects into three risk subgroups of elevated risk for adverse outcomes. The findings emphasize the model's ability to integrate multimodal features to generate ISNLD, enabling early detection and prognostic prediction of NAFLD. This facilitates personalized stratification for NAFLD and metabolism-related outcomes based on digital non-invasive markers, enabling preventive interventions.
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