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Predicting the Outcome and Survival of Patients with Spinal Cord Injury Using Machine Learning Algorithms: A Systematic Review.

Authors :
Habibi MA
Naseri Alavi SA
Soltani Farsani A
Mousavi Nasab MM
Tajabadi Z
Kobets AJ
Source :
World neurosurgery [World Neurosurg] 2024 Aug; Vol. 188, pp. 150-160. Date of Electronic Publication: 2024 May 23.
Publication Year :
2024

Abstract

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 neurologic 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 to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI.<br />Methods: The literature was comprehensively searched for the pertinent studies from inception to May 25, 2023. Therefore, electronic databases of PubMed, Embase, Scopus, and Web of Science were systematically searched with individual search syntax.<br />Results: A total of 9424 individuals diagnosed with SCI across multiple studies were analyzed. Among the 21 studies included, 5 specifically aimed to evaluate diagnostic accuracy, while the remaining 16 focused on exploring prognostic factors or management strategies.<br />Conclusions: ML and deep learning (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.<br /> (Copyright © 2024 Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1878-8769
Volume :
188
Database :
MEDLINE
Journal :
World neurosurgery
Publication Type :
Academic Journal
Accession number :
38796146
Full Text :
https://doi.org/10.1016/j.wneu.2024.05.103