ArticleScientific reports2018
Deep learning enables automated scoring of liver fibrosis stages.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- FibroX: a machine learning model for detecting and prognosticating advanced fibrosis in metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Article
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Modern therapeutic strategies to mitigate biofilm-associated infection in livestock organisms.Antonie van Leeuwenhoek · 2025Review
- A Novel Deep Learning Framework for Liver Fibrosis Staging and Etiology Diagnosis Using Integrated Liver-Spleen Elastography.Diagnostics (Basel, Switzerland) · 2025Article
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.Medical & biological engineering & computing · 2025Article
- Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults.Journal of pathology informatics · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Deep learning-based method for grading histopathological liver fibrosis in rodent models of metabolic dysfunction-associated steatohepatitis.Frontiers in medicine · 2025Article
- Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.Biomedical optics express · 2024Article
- Modelling and targeting mechanical forces in organ fibrosis.Nature reviews bioengineering · 2024Article
- Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.Medical image analysis · 2023Article
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning.Biophysics reviews · 2023Review
- Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks.Educational and psychological measurement · 2023Article
- The role of altered lipid composition and distribution in liver fibrosis revealed by multimodal nonlinear optical microscopy.Science advances · 2023Article
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.Clinical and molecular hepatology · 2022Review
- Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85-0.95 versus ANN (AUROC of up to 0.87-1.00), MLR (AUROC of up to 0.73-1.00), SVM (AUROC of up to 0.69-0.99) and RF (AUROC of up to 0.94-0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.
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