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This paper is in the following e-collection/theme issue:

Reviews in AI (160) Decision Support for Health Professionals (2117) Machine Learning (3048) Spinal Cord Injury Rehabilitation (97) Artificial Intelligence (4533) Applications of AI (854)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66233, first published .
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Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis

Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis

Authors of this article:

Linxing Zhong1 Author Orcid Image ;   Qiying Huang1 Author Orcid Image ;   Hao Zhang1 Author Orcid Image ;   Liang Xue1 Author Orcid Image ;   Yehuang Chen1 Author Orcid Image ;   Jianwu Wu1 Author Orcid Image ;   Liangfeng Wei1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Andrabi S, Steinmetz M, Labhasetwar V. Evolving role of biomarkers in spinal cord injury: From research to clinical translation. Biomedicine & Pharmacotherapy 2026;199:119389 View
  2. Perez-Sanpablo A, Rodriguez-Barragan M, Meneses-Peñaloza A, Quinzaños-Fresnedo J, Barrera-Ortiz A, Palomino-Ramos F, Prado-Escobar O, Rodríguez M, Lopez-Nava I, Ambrosio-Bastian J. Machine Learning–Based Predictive Model for Functional Independence in Spinal Cord Injury: Protocol for a Predictive Rule Development and Validation Study. JMIR Research Protocols 2026;15:e95236 View
  3. Zhao Y, Qiang M, Bai X, Zhang T, Shi T, Lin J, Ma H, Lin W. Prediction of postoperative outcomes in spinal cord surgery via fusion of point-cloud geometric features and radiomics. Information Fusion 2026;136:104509 View
  4. Liu Y, Meng X, Ding Y, Yao R, Xu S. Machine Learning–Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review. JMIR Medical Informatics 2026;14:e84980 View