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The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review
Journal article   Open access   Peer reviewed

The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review

Teleale F Gebeyehu, Mohammad Amin Sabbaghalvani, Giovanna Failla, Ashmal S Kabani, Yashvi Shah, Alexander Kharichev, Joshua A Dian, Stavros Matsoukas, Alexander R Vaccaro, Gregory D Schroeder, …
Spinal cord, v 64(1), pp 3-13
01 Jan 2026
DOI: https://doi.org/10.1038/s41393-025-01155-0
PMID: 41345782
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1038/s41393-025-01155-0View
Published, Version of Record (VoR) Open

Abstract

Artificial Intelligence Humans Spinal Cord Injuries - diagnosis Spinal Cord Injuries - rehabilitation Spinal Cord Injuries - therapy Machine Learning
Systematic Review. To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions. PubMed, Scopus and Cochrane libraries were searched (March 2025). Studies published in English between January 1 , 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of study, total participants, American Spinal Injury Association (ASIA) Impairment Scale (AIS), machine learning (ML) models, inputs, outcomes and performance metrices. A total of 23 studies with 120,931 individuals were identified. Classical Machine Learning Models, Ensemble Learning Models and Deep Learning Models were the most used ML families. Age, AIS, neurologic level of injury, sex, mechanism of injury and motor score were the most common inputs. Predictions of neurologic status, functionality status, Hospital/ICU utilizations, complications, survival, discharge destination and results of image segmentation and patient grouping were the outputs of interest. The performance metrices were satisfactory in most and higher than humans in some studies. AI can facilitate personalized approach to diagnosis of SCI, prediction of outcomes like neurological improvement, complications, functionality indicators like walking, selfcare and independence, re-admissions, prolonged length of stays, discharge destination and mortality after injury. It was also useful to suggest specific MAP goals and time of surgical intervention. These functions complement clinical judgement.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Clinical Neurology
Rehabilitation
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