ArticleEntropy (Basel, Switzerland)2020
Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Ultrasound detection of nonalcoholic steatohepatitis using convolutional neural networks with dual-branch global-local feature fusion architecture.Medical & biological engineering & computing · 2026Article
- Clinical Validation of a Deep Learning-Based 2D Ultrasound Steatosis Algorithm: Cutoff Transferability, Scanner Generalizability, and Comparison with FibroScan.Diagnostics (Basel, Switzerland) · 2026Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
- Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.Scientific reports · 2024Article
- Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.Diagnostics (Basel, Switzerland) · 2023Article
- Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.European radiology · 2023Article
- Automated fatty liver disease detection in point-of-care ultrasound B-mode images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Information Entropy and Its Applications.Advances in experimental medicine and biology · 2023Article
- Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.Bioengineering (Basel, Switzerland) · 2022Review
- Review
- Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks.Neural processing letters · 2022Article
- Accurate and generalizable quantitative scoring of liver steatosis from ultrasound imagesWorld journal of gastroenterology · 2022Article
- Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy.Diagnostics (Basel, Switzerland) · 2021Article
- Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis.IEEE journal of translational engineering in health and medicine · 2021Article
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7 authors.
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Abstract
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (
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