Back to search

Article

Machine Learning–Based Prediction of Ultrasound-Detected Metabolic Dysfunction–Associated Steatotic Liver Disease Using Routine Clinical and Biochemical Parameters

2025-11-10

Abstract excerpt

<h4>Background: </h4> /Objectives: Metabolic dysfunction–associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease globally, mirroring the increasing prevalence of obesity, insulin resistance, and type 2 diabetes. Early detection of hepatic steatosis is vital for cardiometabolic risk assessment; however, conventional imaging is costly and impractical for population screening. Thi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
9270f5a5-add0-5b1f-b428-1cd2241d5f42
DOI
10.20944/preprints202511.0648.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine Learning–Based Prediction of Ultrasound-Detected Metabolic Dysfunction–Associated Steatotic Liver Disease Using Routine Clinical and Biochemical ParametersDOI 10.20944/preprints202511.0648.v1
Machine Learning Enables Single-Score Assessment of MASLD Presence and SeveritySemantic proximity
Extracellular Vesicles as Biomarkers for Steatosis Stages in MASLD Patients: an Algorithmic Approach Using Explainable Artificial IntelligenceSemantic proximity
Extracellular Vesicles as Biomarkers for Steatosis Stages in MASLD Patients: an Algorithmic Approach Using Explainable Artificial IntelligenceSemantic proximity
Interpretable Machine Learning Predicts Hepatic Fat Response to a Monounsaturated Fat-Enriched Diet in Steatotic Liver DiseaseSemantic proximity
Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult PopulationSemantic proximity
Clinical and biochemical phenotypes of Lean MAFLD: machine learning-based biomarker identification and statistical association analysis in a cross-sectional study.Semantic proximity
ADVANCED ARTIFICIAL INTELLIGENCE ENABLED METHODS FOR EARLY DETECTION OF NON-ALCOHOLIC FATTY LIVER DISEASE AND ASSOCIATED HEALTH RISKSSemantic proximity
Machine learning-based mortality prediction models for non-alcoholic fatty liver disease in the general United States populationSemantic proximity
Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional StudySemantic proximity
Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT studySemantic proximity
Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.Semantic proximity
AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center StudySemantic proximity
Select a neighboring publication to make it the new centre.