ArticleScientific reports2018
A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled 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
- AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH and AIH.Journal of clinical pathology · 2026Observational
- Diagnosis and grading of steatotic liver disease via clinical and laboratory data using machine learning.Scientific reports · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 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
- FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.BMC medical imaging · 2025Article
- Identification of a Risk-Prediction Model for Hypertension Patients Concomitant with Nonalcoholic Fatty Liver Disease.Healthcare (Basel, Switzerland) · 2025Article
- Impact of genotyping (Frontiers in endocrinology · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.Scientific reports · 2024Article
- Comparison of fatty liver index with fibroscan in non-alcoholic fatty liver disease.Journal of family medicine and primary care · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- A scoping review of fair machine learning techniques when using real-world data.Journal of biomedical informatics · 2024Article
- Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.Frontiers in medical technology · 2024Article
- Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study.Open medicine (Warsaw, Poland) · 2024Article
- Fibro-Scope V1.0.1: an artificial intelligence/neural network system for staging of nonalcoholic steatohepatitis.Hepatology international · 2023Article
- Artificial intelligence models for the diagnosis and management of liver diseases.Ultrasonography (Seoul, Korea) · 2023Article
- Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features.International journal of environmental research and public health · 2022Article
- Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.