ArticlePloS one2022
Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Pooled it
- Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review.PloS one · 2024Pooled it
- Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Ultrasound Elastography in Chronic Liver Disease: From Clinical Applications to Quality Assurance and Future Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- A combi-elasto ultrasound based deep learning model on liver fibrosis staging for suspected MASLD/MASH patients.BMC medical imaging · 2026Article
- The value of sound touch elastography in the noninvasive staging of fibrosis in IgA nephropathy.BMC medical imaging · 2026Article
- Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.iLIVER · 2026Article
- Accurate and Scalable Classification of Colonoscopy Neoplasia Using Machine Learning and Natural Language Processing.Clinical and translational gastroenterology · 2026Article
- Development and evaluation of machine learning models for predicting significant liver fibrosis stages: A retrospective analysis.Canadian liver journal · 2026Article
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Article
- Network Modeling of Biomarker Systems in Liver Steatosis and Fibrosis.Clinical and translational science · 2025Article
- Noninvasive assessment of pediatric glomerular disease: multimodal ultrasound.Quantitative imaging in medicine and surgery · 2025Article
- Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Review
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.Obesity surgery · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo develop a quantitative ultrasound (QUS)- and elastography-based model to improve classification of steatosis grade, inflammation grade, and fibrosis stage in patients with chronic liver disease in comparison with shear wave elastography alone, using histopathology as the reference standard.
methodsThis ancillary study to a prospective institutional review-board approved study included 82 patients with non-alcoholic fatty liver disease, chronic hepatitis B or C virus, or autoimmune hepatitis. Elastography measurements, homodyned K-distribution parametric maps, and total attenuation coefficient slope were recorded. Random forests classification and bootstrapping were used to identify combinations of parameters that provided the highest diagnostic accuracy. Receiver operating characteristic (ROC) curves were computed.
resultsFor classification of steatosis grade S0 vs. S1-3, S0-1 vs. S2-3, S0-2 vs. S3, area under the receiver operating characteristic curve (AUC) were respectively 0.60, 0.63, and 0.62 with elasticity alone, and 0.90, 0.81, and 0.78 with the best tested model combining QUS and elastography features. For classification of inflammation grade A0 vs. A1-3, A0-1 vs. A2-3, A0-2 vs. A3, AUCs were respectively 0.56, 0.62, and 0.64 with elasticity alone, and 0.75, 0.68, and 0.69 with the best model. For classification of liver fibrosis stage F0 vs. F1-4, F0-1 vs. F2-4, F0-2 vs. F3-4, F0-3 vs. F4, AUCs were respectively 0.66, 0.77, 0.72, and 0.74 with elasticity alone, and 0.72, 0.77, 0.77, and 0.75 with the best model.
conclusionRandom forest models incorporating QUS and shear wave elastography increased the classification accuracy of liver steatosis, inflammation, and fibrosis when compared to shear wave elastography alone.
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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.