ArticleThe Journal of clinical endocrinology and metabolism2024
Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.
Article in The Journal of clinical endocrinology and metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 12 citations in OpenAlex.
- The Emerging Potential of Diabetes Technology to Improve Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients with Type 1 Diabetes: A Narrative Review.Biomedicines · 2026Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Machine Learning Models to Predict Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) With Simple Anthropometric and Biochemical Variables: A Cross-Sectional Study in US Population.International journal of hepatology · 2026Article
- An interpretable machine learning model for predicting metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes.Diabetes, obesity & metabolism · 2026Article
- Interpretable Machine Learning Models for Early Detection of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Non-Invasive Routine Clinical and Laboratory Data.International journal of general medicine · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
- Extracellular vesicle-mediated approaches for the diagnosis and therapy of MASLD: current advances and future prospective.Lipids in health and disease · 2025Review
- ISIFrontiers in medicine · 2025Article
- Artificial intelligence is going to transform the field of endocrinology: an overview.Frontiers in endocrinology · 2025Article
- The Mediation Role of Insulin Resistance and Chronic Systemic Inflammation in the Association Between Obesity and NAFLD: Two Cross-Sectional and a Mendelian Randomization Study.Clinical epidemiology · 2025Article
- Protocol for a Longitudinal Cohort Study to Understand Characteristics and Risk Factors Underlying Vibration-Controlled Transient Elastography-Diagnosed Metabolic Dysfunction-Associated Fatty Liver Disease Children.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
Abstract
contextThe presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.
objectiveTo develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM.
methodsFeature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The performance of the multiple logistic regression model was quantified by sensitivity, specificity, and percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments.
resultsWe studied 2000 patients with DM (mean age 58.85 ± 17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95% CI, 0.82-0.86), while DCA showed a higher clinical utility of the model, ranging from 30% to 84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74; AUC: 0.81 [95% CI, 0.76-0.87]), whereas unsupervised clustering identified high-risk patients.
conclusionA ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD.
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