Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis
The ML models predict SCI outcomes with relative accuracy, particularly in spinal cord function prognosis. They are expected to become important tools for clinicians in assessing the prognosis of patients with SCI, with the XGBoost algorithm showing the best performance. Prediction models should continue to advance as large data are used and ML algorithms develop.
Authors
- Jianwu Wu (ORCID: https://orcid.org/0000-0001-7890-2379)
- Yehuang Chen (ORCID: https://orcid.org/0009-0000-7215-5456)
- QiYing Huang (ORCID: https://orcid.org/0009-0004-2463-8989)
- Liang Xue (ORCID: https://orcid.org/0000-0003-3467-7936)
- Hao Zhang (ORCID: https://orcid.org/0009-0004-7373-7801)
- Liangfeng Wei (ORCID: https://orcid.org/0000-0003-2748-6306)
- Linxing Zhong (ORCID: https://orcid.org/0009-0008-5948-4931)
Institutions
- Fujian Medical University (CN)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-08-17
- DOI
- https://doi.org/10.2196/66233
- Citations
- 1
- Primary Topic
- Spinal Cord Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.47