ArticlePloS one2019
Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis.Journal of advanced research · 2026Pooled it
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- Pooled it
- Evaluation of the Effect Derived from Silybin with Vitamin D and Vitamin E Administration on Clinical, Metabolic, Endothelial Dysfunction, Oxidative Stress Parameters, and Serological Worsening Markers in Nonalcoholic Fatty Liver Disease Patients.Oxidative medicine and cellular longevity · 2019Trial
- Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.Physical and engineering sciences in medicine · 2026Article
- High Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients with Hidradenitis Suppurativa: A Guide to Easily Assess the Clinical Risk of Comorbid Liver Disease.JID innovations : skin science from molecules to population health · 2026Article
- Machine learning-based identification of targeted metabolomic biomarkers for early diagnosis and fibrosis-stage discrimination in metabolic dysfunction-associated steatotic liver disease.Frontiers in nutrition · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Machine learning for non-invasive diagnosis and risk stratification.World journal of hepatology · 2025Review
- Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.Scientific reports · 2025Article
- Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Global Trends in Non-Invasive Techniques for the Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease: A Bibliometric and Visualization Analysis.Journal of multidisciplinary healthcare · 2025Review
- Impact of genotyping (Frontiers in endocrinology · 2025Article
- Liver Diseases: Science, Fiction and the Foreseeable Future.Journal of personalized medicine · 2024Article
- Nonalcoholic steatohepatitis: A comprehensive updated review of risk factors, symptoms, and treatment.Heliyon · 2024Review
- Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters.Scientific reports · 2024Article
- Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.PloS one · 2024Article
- Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning.Gastro hep advances · 2024Article
- Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.Clinical and molecular hepatology · 2023Review
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND &
aimsCurrent non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches.
methodsNon-invasive parameters were selected by an ensemble feature selection (EFS) from a retrospectively collected training cohort of 164 obese individuals (age: 43.5±10.3y; BMI: 54.1±10.1kg/m2) to develop a model able to predict the histological assessed NAFLD activity score (NAS). The model was evaluated in an independent validation cohort (122 patients, age: 45.2±11.75y, BMI: 50.8±8.61kg/m2).
resultsEFS identified age, γGT, HbA1c, adiponectin, and M30 as being highly associated with NAFLD. The model reached a Spearman correlation coefficient with the NAS of 0.46 in the training cohort and was able to differentiate between NAFL (NAS≤4) and NASH (NAS>4) with an AUC of 0.73. In the independent validation cohort, an AUC of 0.7 was achieved for this separation. We further analyzed the potential of the new model for disease monitoring in an obese cohort of 38 patients under lifestyle intervention for one year. While all patients lost weight under intervention, increasing scores were observed in 15 patients. Increasing scores were associated with significantly lower absolute weight loss, lower reduction of waist circumference and basal metabolic rate.
conclusionsA newly developed model (http://CHek.heiderlab.de) can predict presence or absence of NASH with reasonable performance. The new score could be used to detect NASH and monitor disease progression or therapy response to weight loss interventions.
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