Predicting the Outcome and Survival of Patients with Spinal Cord Injury using Machine Learning Algorithms; A Systematic Review

Background Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurological outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. Method The literature was comprehensively searched for the pertinent studies from inception to 25 May 2023. Therefore, electronic databases of PubMed, Embase, Scopus, and Web of Science were systematically searched with individual search syntax. Results A total of 9424 individuals diagnosed with SCI across multiple studies were analyzed. Among the 21 studies included, five specifically aimed to evaluate diagnostic accuracy, while the remaining 16 focused on exploring prognostic factors or management strategies. Conclusion ML and DL have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.