ArticleBMC medical informatics and decision making2012
Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- 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
- Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging.BioMed research international · 2020Trial
- A simple clinical model for predicting multiple liver histologic injuries in obese individuals undergoing bariatric surgery.Hepatology international · 2026Article
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Early Kasai portoenterostomy in a 9-day-old newborn with extrahepatic biliary atresia: a case report highlighting improved prognosis with prompt intervention.The Journal of international medical research · 2025Article
- Improving spleen segmentation in ultrasound images using a hybrid deep learning framework.Scientific reports · 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
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Artificial intelligence models for the diagnosis and management of liver diseases.Ultrasonography (Seoul, Korea) · 2023Article
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- Artificial intelligence in liver ultrasound.World journal of gastroenterology · 2022Review
- Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia.Frontiers in pediatrics · 2020Article
- Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology.Hepatology international · 2019Review
- Deep learning enables automated scoring of liver fibrosis stages.Scientific reports · 2018Article
- Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.Gastroenterology research and practice · 2016Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial neural networks (ANNs) are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy.
methods239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden's index and receiver operating characteristic (ROC) analysis.
results5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV) waveform, hepatic artery pulsatile index (HAPI) and HV damping index (HVDI), were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden's index (YI) was 0.80.
conclusionsThe established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice.
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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.