Deep learning-enhanced quantitative evaluation of hepatic ischemia–reperfusion injury and steatosis via near-infrared hyperspectral imaging
Our findings highlight the potential of NIR-HSI enhanced by artificial intelligence in both basic and clinical research for assessing the severity of hepatic steatosis and I/R injury, predicting inflammatory marker levels, and identifying postoperative complication of EAD after LT.
Authors
- Y. Wang
- Min Tian (ORCID: https://orcid.org/0000-0002-1198-1073)
- Cuiping Yao (ORCID: https://orcid.org/0000-0002-7110-0696)
- Jifu Du
- J Zhang
- Lexuan Xu (ORCID: https://orcid.org/0009-0008-4218-5900)
- Z. Wang (ORCID: https://orcid.org/0000-0002-6940-173X)
- Lu Lu (ORCID: https://orcid.org/0000-0002-6556-8022)
- Zhan Qu (ORCID: https://orcid.org/0000-0002-7920-3016)
- Fan Mu
- Chun Zhang
- Zhen Zhao
- Xin Zhang
- Liangshuo Hu
- Yi Lyu
- Boyan Tian
- Jianwen Lu
- Tao Li
- Hao Sun
- Chen Chen
- Bo Wang
Institutions
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- Xi'an Peihua University (CN)
- Xi'an Institute of Optics and Precision Mechanics (CN)
- First Hospital of Xi'an (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-01-12
- DOI
- https://doi.org/10.1097/js9.0000000000004819
- Primary Topic
- Organ Transplantation Techniques and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00