The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review
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...
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Elsevier
2025-01-01
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| Series: | Brain and Spine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277252942500027X |
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| author | Saran Singh Gill Hariharan Subbiah Ponniah Sho Giersztein Rishi Miriyala Anantharaj Srikar Reddy Namireddy Joshua Killilea DanieleS.C. Ramsay Ahmed Salih Ahkash Thavarajasingam Daniel Scurtu Dragan Jankovic Salvatore Russo Andreas Kramer Santhosh G. Thavarajasingam |
| author_facet | Saran Singh Gill Hariharan Subbiah Ponniah Sho Giersztein Rishi Miriyala Anantharaj Srikar Reddy Namireddy Joshua Killilea DanieleS.C. Ramsay Ahmed Salih Ahkash Thavarajasingam Daniel Scurtu Dragan Jankovic Salvatore Russo Andreas Kramer Santhosh G. Thavarajasingam |
| author_sort | Saran Singh Gill |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cd851b67d8944bbb8003d5e6309a6bbb |
| institution | OA Journals |
| issn | 2772-5294 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain and Spine |
| spelling | doaj-art-cd851b67d8944bbb8003d5e6309a6bbb2025-08-20T02:13:10ZengElsevierBrain and Spine2772-52942025-01-01510420810.1016/j.bas.2025.104208The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic reviewSaran Singh Gill0Hariharan Subbiah Ponniah1Sho Giersztein2Rishi Miriyala Anantharaj3Srikar Reddy Namireddy4Joshua Killilea5DanieleS.C. Ramsay6Ahmed Salih7Ahkash Thavarajasingam8Daniel Scurtu9Dragan Jankovic10Salvatore Russo11Andreas Kramer12Santhosh G. Thavarajasingam13Imperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Faculty of Medicine, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Faculty of Medicine, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Faculty of Medicine, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomImperial Brain & Spine Initiative, Imperial College London, London, United KingdomDepartment of Neurosurgery, Universitätsmedizin Mainz, Mainz, GermanyDepartment of Neurosurgery, LMU University Hospital, LMU, Munich, GermanyImperial College Healthcare NHS Trust, London, United KingdomDepartment of Neurosurgery, LMU University Hospital, LMU, Munich, GermanyImperial Brain & Spine Initiative, Imperial College London, London, United Kingdom; Department of Neurosurgery, LMU University Hospital, LMU, Munich, Germany; Corresponding author. Department of Neurosurgery, LMU University Hospital, LMU, Munich, Germany.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.http://www.sciencedirect.com/science/article/pii/S277252942500027XtSCISpinal cord injurySpineAIDiagnosisPrognosis |
| spellingShingle | Saran Singh Gill Hariharan Subbiah Ponniah Sho Giersztein Rishi Miriyala Anantharaj Srikar Reddy Namireddy Joshua Killilea DanieleS.C. Ramsay Ahmed Salih Ahkash Thavarajasingam Daniel Scurtu Dragan Jankovic Salvatore Russo Andreas Kramer Santhosh G. Thavarajasingam The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review Brain and Spine tSCI Spinal cord injury Spine AI Diagnosis Prognosis |
| title | The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review |
| title_full | The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review |
| title_fullStr | The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review |
| title_full_unstemmed | The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review |
| title_short | The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review |
| title_sort | diagnostic and prognostic capability of artificial intelligence in spinal cord injury a systematic review |
| topic | tSCI Spinal cord injury Spine AI Diagnosis Prognosis |
| url | http://www.sciencedirect.com/science/article/pii/S277252942500027X |
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