Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis
Our proposed model could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.
Authors
- Xueling Guo (ORCID: https://orcid.org/0000-0002-8194-8295)
- James Guoxian Huang (ORCID: https://orcid.org/0000-0001-5495-3577)
- T. I. Hsu (ORCID: https://orcid.org/0009-0008-1233-002X)
- Tue Ngo
- Yong-Chang Feng
- Sijun Meng
- Chengxiang Lu (ORCID: https://orcid.org/0000-0002-0213-9423)
- Wei-Lin Change
- Peng Lin (ORCID: https://orcid.org/0000-0003-3760-5575)
- Sitong Liu (ORCID: https://orcid.org/0000-0001-6293-2020)
- Ling-Hui Zhan
- Jiali Zhou (ORCID: https://orcid.org/0000-0002-5055-9537)
- Yiqun Hu (ORCID: https://orcid.org/0000-0001-9157-7865)
- Manuel L. B. Palacio
- Glen Qin
- Wen-Song Su
Institutions
- Xiamen University (CN)
- Zhongshan Hospital of Xiamen University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-31
- DOI
- https://doi.org/10.21037/qims-24-841
- Citations
- 3
- Primary Topic
- Pancreatitis Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- Xiamen Municipal Bureau of Science and Technology