Deep Learning‐Enhanced <scp>CTA</scp> for Noninvasive Prediction of First Variceal Haemorrhage in Cirrhosis: A Multi‐Centre Study
The novel deep learning-enhanced CTA models offer a robust, non-invasive method for predicting FVH, with the ViT + RL* model demonstrating excellent efficacy; thus providing a valuable tool for early risk stratification in cirrhotic patients.
Authors
- Fubi Hu (ORCID: https://orcid.org/0000-0003-1637-2493)
- Wei Ren (ORCID: https://orcid.org/0000-0002-2357-0204)
- Tengfei Li (ORCID: https://orcid.org/0009-0004-4435-7527)
- Bin Song (ORCID: https://orcid.org/0000-0002-7269-2101)
- Xiaohe Tian (ORCID: https://orcid.org/0000-0002-2294-3945)
- Minghui Liu (ORCID: https://orcid.org/0000-0002-3171-0270)
- Wei Yi (ORCID: https://orcid.org/0000-0003-0106-7701)
- Yuan Gao (ORCID: https://orcid.org/0000-0001-5351-6922)
- Shang Wan (ORCID: https://orcid.org/0000-0001-9182-1709)
Institutions
- University of Electronic Science and Technology of China (CN)
- General Electric (Spain) (ES)
- Sichuan University (CN)
- Wenzhou Medical University (CN)
- West China Medical Center of Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Quzhou University (CN)
- First Affiliated Hospital of Chengdu Medical College (CN)
- Sanya Central Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Liver International
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1111/liv.70274
- Citations
- 2
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 2.64
Funders
- China Postdoctoral Science Foundation