ArticleHepatology international2022
Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network.
Article in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework.Medical & biological engineering & computing · 2026Article
- Multimodal artificial intelligence models for liver fibrosis staging: a scoping review.Abdominal radiology (New York) · 2026Review
- [Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- From bench to bedside: Molecular mechanisms, diagnostic tools, and therapeutic strategies in liver fibrosis.Liver research (Beijing, China) · 2026Review
- Correlation analysis of Th1/Th2 cytokines and liver fibrosis indicators in chronic hepatitis B patients.Journal of medical biochemistry · 2026Article
- Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- A Novel Deep Learning Framework for Liver Fibrosis Staging and Etiology Diagnosis Using Integrated Liver-Spleen Elastography.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.European radiology · 2025Article
- Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.Clinical transplantation · 2025Article
- A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen.Sensors (Basel, Switzerland) · 2023Article
- miR-488-5p mitigates hepatic stellate cell activation and hepatic fibrosis via suppressing TET3 expression.Hepatology international · 2023Article
- Review
- Deep learning and non-invasive assessment of significant fibrosis: does adding more toppings improve the flavor of prediction?Hepatology international · 2022Article
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Funding
Abstract
BACKGROUND AND
aimsChronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients.
methodsOf 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients' clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC).
resultsDI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, p
conclusionThe joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients.
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