Artificial intelligence-based prediction model for surgical site infection in metastatic spinal disease: a multicenter development and validation study
This study develops and validates an AI tool with strong predictive performance in identifying patients at a high risk for SSI. By facilitating personalized treatment based on risk classification, this advancement has the potential to significantly enhance surgical care for patients with metastatic spinal disease. Future research should focus on integrating this predictive tool into clinical practice and exploring its applicability across diverse patient populations.
Authors
- Yaosheng Liu
- Yuanxing Pan (ORCID: https://orcid.org/0000-0002-7588-0538)
- Yong Qin (ORCID: https://orcid.org/0000-0003-0541-8054)
- Yunpeng Cui (ORCID: https://orcid.org/0000-0001-7294-1473)
- Yuncen Cao (ORCID: https://orcid.org/0009-0006-8675-6627)
- Qiwei Wang (ORCID: https://orcid.org/0009-0001-1335-0347)
- Xiongwei Zhao
- Xuedong Shi
- Xiaotong Che
- Bing Wang (ORCID: https://orcid.org/0000-0003-4410-0495)
- Shengjie Wang (ORCID: https://orcid.org/0000-0001-8637-7308)
- Mingxing Lei (ORCID: https://orcid.org/0009-0009-7134-0014)
Institutions
- Harbin Medical University (CN)
- Shanghai Jiao Tong University (CN)
- Anhui Medical University (CN)
- Peking University (CN)
- Chinese PLA General Hospital (CN)
- Hainan General Hospital (CN)
- Peking University First Hospital (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
- Shanghai Sixth People's Hospital (CN)
- Hainan Medical College Hospital (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1097/js9.0000000000002806
- Citations
- 4
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.70