ArticleSensors (Basel, Switzerland)2021
Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.Abdominal radiology (New York) · 2026Pooled 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
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- A Novel Method for Motion Blur Detection and Quantification Using Signal Analysis on a Controlled Empirical Image Dataset.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Advances in quantitative ultrasound for metabolic dysfunction-associated steatotic liver disease diagnosis.Frontiers in physiology · 2026Review
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 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
- Mixture of Expert-Based SoftMax-Weighted Box Fusion for Robust Lesion Detection in Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Harnessing the Power of AI for Enhanced Diagnosis and Treatment of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Review
- Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.Scientific reports · 2024Article
- Review
- Bio-Inspired Network for Diagnosing Liver Steatosis in Ultrasound Images.Bioengineering (Basel, Switzerland) · 2023Article
- Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations.Sensors (Basel, Switzerland) · 2023Article
- Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.Frontiers in genetics · 2023Review
- Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.
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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.