The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review

Gill, Saran Singh; Ponniah, Hariharan Subbiah; Giersztein, Sho; Anantharaj, Rishi Miriyala; Namireddy, Srikar Reddy; Killilea, Joshua; Ramsay, DanieleS.C.; Salih, Ahmed; Thavarajasingam, Ahkash; Scurtu, Daniel; Jankovic, Dragan; Russo, Salvatore; Kramer, Andreas; Thavarajasingam, Santhosh G. (2025): The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review. Brain and Spine, 5: 104208. ISSN 27725294

Abstract

Background

Artificial intelligence (AI) models have shown potential for diagnosing and prognosticating traumatic spinal cord injury (tSCI), but their clinical utility remains uncertain.

Method

ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. PROBAST and TRIPOD tools were used to assess the quality and reporting of included studies (PROSPERO: CRD42023464722). Fourteen studies, comprising 20 models and 280,817 pooled imaging datasets, were included. Analysis was conducted in line with the SWiM guidelines.

Results

For prognostication, 11 studies predicted outcomes including AIS improvement (30%), mortality and ambulatory ability (20% each), and discharge or length of stay (10%). The mean AUC was 0.770 (range: 0.682–0.902), indicating moderate predictive performance. Diagnostic models utilising DTI, CT, and T2-weighted MRI with CNN-based segmentation achieved a weighted mean accuracy of 0.898 (range: 0.813–0.938), outperforming prognostic models.

Conclusion

AI demonstrates strong diagnostic accuracy (mean accuracy: 0.898) and moderate prognostic capability (mean AUC: 0.770) for tSCI. However, the lack of standardised frameworks and external validation limits clinical applicability. Future models should integrate multimodal data, including imaging, patient characteristics, and clinician judgment, to improve utility and alignment with clinical practice.

• Dokumententyp: Artikel (Klinikum der LMU)
• Organisationseinheit (Fakultäten): 07 Medizin > Klinikum der LMU München > Neurochirurgische Klinik und Poliklinik
• DFG-Fachsystematik der Wissenschaftsbereiche: Lebenswissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892