Introduction: Spinal cord injury (SCI) is a significant and transforming event, with an estimated annual incidence of 40 cases per million individuals in North America. Considering the significance of accurate diagnosis and effective therapy in managing SCI, Machine Learning (ML) and Robot-Assisted Gait Training (RAGT) technologies hold promise for enhancing optimal practices and elevating the quality of care. This study aims to determine the impact of the ML and RAGT techniques employed on the outcome results of SCI.
Methods: We reviewed four databases, including PubMed, Scopus, ScienceDirect, and the Cochrane Central Register of Controlled Trials (CENTRAL), until 20 August 2023. The keywords used in this study encompassed the following: a comprehensive search was executed on research exclusively published in the English language: machine learning, robotics, and spinal cord injury.
Results: A comprehensive search was conducted across four databases, identifying 2367 articles following rigorous data filtering. The results of the odd ratio (OR) and confidence interval (CI) of 95% for the ASIA Impairment Scale, or AIS grade A, were 0.093 (0.011-0.754, p = 0.026), for AIS grade B, 0.875 (0.395-1.939, p = 0.743), for AIS grade C, 3.626 (1.556-8.449, p = 0.003), and for AIS grade D, 8.496 (1.394-51.768, p = 0.020). The robotic group exhibited a notable reduction in AS (95% CI = -0.239 to -0.045, p = 0.004) and MAS (95% CI = -3.657 to -1.066, p ≤ 0.001) measures. This study also investigated spasticity and walking ability, which are significant.
Conclusions: The ML approach exhibited enhanced precision in forecasting AIS result scores. Implementing RAGT has been shown to impact spasticity reduction and improve walking ability.
Keywords: machine learning; robot-assisted gait training; spinal cord injury.