ArticleScientific reports2024
An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07305324 (Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care), which is not on this map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled 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
- AI-Based Screening for At-Risk MASLD and Advanced Fibrosis in the US Population: A Cost-Effectiveness Analysis.Digestive diseases and sciences · 2026Article
- A conceptual framework for measuring AI health equity.International journal for equity in health · 2026Article
- Development and validation of a risk model for non-fatty chronic liver disease in middle-aged and older adults with metabolic syndrome.BMC gastroenterology · 2026Article
- An Explainable AI Framework for Continuous Monitoring, Risk Stratification, and Clinical Decision Support in Primary Biliary Cholangitis: Protocol for a Multiphase Development and Validation Study.JMIR research protocols · 2026Article
- A conceptual agentic AI architecture for MASLD-associated significant fibrosis in primary care.PLOS digital health · 2026Article
- Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.Plant methods · 2026Article
- An Explainable AI Tool (FibroX) for Detecting Advanced Liver Fibrosis in Adults With Type 2 Diabetes: Protocol for a Pilot Crossover Trial.JMIR research protocols · 2026Article
- Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review.Health science reports · 2026Review
- A clinician-oriented machine learning model for adult asthma exacerbation prediction: comparative analysis of nine algorithms.Journal of thoracic disease · 2026Article
- Cardiovascular Implications in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review.Korean circulation journal · 2026Review
- Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting metabolic dysfunction-associated steatotic liver disease risk using patient-derived induced pluripotent stem cells.Stem cells translational medicine · 2026Article
- FibroX: a machine learning model for detecting and prognosticating advanced fibrosis in metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Article
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- Biomarkers for early identification of metabolic dysfunction-associated steatotic liver disease (MASLD): a narrative review.Archives of medical science : AMS · 2026Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
Abstract
Early identification of high-risk metabolic dysfunction-associated steatohepatitis (MASH) can offer patients access to novel therapeutic options and potentially decrease the risk of progression to cirrhosis. This study aimed to develop an explainable machine learning model for high-risk MASH prediction and compare its performance with well-established biomarkers. Data were derived from the National Health and Nutrition Examination Surveys (NHANES) 2017-March 2020, which included a total of 5281 adults with valid elastography measurements. We used a FAST score ≥ 0.35, calculated using liver stiffness measurement and controlled attenuation parameter values and aspartate aminotransferase levels, to identify individuals with high-risk MASH. We developed an ensemble-based machine learning XGBoost model to detect high-risk MASH and explored the model's interpretability using an explainable artificial intelligence SHAP method. The prevalence of high-risk MASH was 6.9%. Our XGBoost model achieved a high level of sensitivity (0.82), specificity (0.91), accuracy (0.90), and AUC (0.95) for identifying high-risk MASH. Our model demonstrated a superior ability to predict high-risk MASH vs. FIB-4, APRI, BARD, and MASLD fibrosis scores (AUC of 0.95 vs. 0.50, 0.50, 0.49 and 0.50, respectively). To explain the high performance of our model, we found that the top 5 predictors of high-risk MASH were ALT, GGT, platelet count, waist circumference, and age. We used an explainable ML approach to develop a clinically applicable model that outperforms commonly used clinical risk indices and could increase the identification of high-risk MASH patients in resource-limited settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.