ArticleEuropean radiology2020
Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
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Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.Abdominal radiology (New York) · 2024Pooled it
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis.BMC gastroenterology · 2021Pooled it
- Quantitative liver ultrasound: current status and challenges.Ultrasonography (Seoul, Korea) · 2026Article
- Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images.Nature communications · 2026Article
- Ultrasound detection of nonalcoholic steatohepatitis using convolutional neural networks with dual-branch global-local feature fusion architecture.Medical & biological engineering & computing · 2026Article
- A precision-constrained framework for evaluating noninvasive biomarkers in MASLD and beyond.Nature communications · 2026Article
- The evolving role of OMICS in gastrointestinal tumor biology and clinical practice.Molecular cancer · 2026Review
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Article
- ResNet50-SCPA-net for liver cirrhosis stage classification using MRI images.Frontiers in artificial intelligence · 2026Article
- LivSCP: Improving Liver Fibrosis Classification Through Supervised Contrastive Pretraining.Diagnostics (Basel, Switzerland) · 2025Article
- Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Comprehensive images and serum biomarkers for biliary atresia and other cholestasis in pediatrics.Scientific data · 2025Article
- Diagnostic Applications of Artificial Intelligence in Liver Diseases.Journal of clinical medicine · 2025Review
- Deep learning-based automated assessment of hepatic fibrosis via magnetic resonance images and nonimage data.Quantitative imaging in medicine and surgery · 2025Article
- Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesThe aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.
methodsDatasets from two tertiary academic referral centers were used. A total of 13,608 ultrasonography images from 3446 patients who underwent surgical resection, biopsy, or transient elastography were used for training a DCNN for the prediction of the METAVIR score. Pathological specimens or estimated METAVIR scores derived from transient elastography were used as a reference standard. A four-class model (F0 vs. F1 vs. F23 vs. F4) was developed. Diagnostic performance of the algorithm was validated on a separate internal test set of 266 patients with 300 images and external test set of 572 patients with 1232 images. Performance in classification of cirrhosis was compared between the DCNN and five radiologists.
resultsThe accuracy of the four-class model was 83.5% and 76.4% on the internal and external test set, respectively. The area under the receiver operating characteristic curve (AUC) for classification of cirrhosis (F4) was 0.901 (95% confidence interval [CI], 0.865-0.937) on the internal test set and 0.857 (95% CI, 0.825-0.889) on the external test set, respectively. The AUC of the DCNN for classification of cirrhosis (0.857) was significantly higher than that of all five radiologists (AUC range, 0.656-0.816; p value < 0.05) using the external test set.
conclusionsThe DCNN showed high accuracy for determining METAVIR score using ultrasonography images and achieved better performance than that of radiologists in the diagnosis of cirrhosis. KEY POINTS: • DCNN accurately classified the ultrasonography images according to the METAVIR score. • The AUROC of this algorithm for cirrhosis assessment was significantly higher than that of radiologists. • DCNN using US images may offer an alternative tool for monitoring liver fibrosis.
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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.