Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review

Journal of Clinical Orthopaedics and Trauma, 2022 · DOI: https://doi.org/10.1016/j.jcot.2022.102046 · Published: October 20, 2022

Spinal Cord Injury Healthcare Bioinformatics

Simple Explanation

Machine learning (ML) algorithms are being used to improve the diagnosis and prediction of outcomes for individuals with acute traumatic spinal cord injuries (SCI). The purpose of this review is to explore the potential for integrating ML into clinical settings to address the diverse nature of injuries and recoveries observed in this patient population. Acute traumatic spinal cord injury (SCI) can lead to temporary or permanent motor and sensory impairment, resulting in significant short-term and long-term health consequences. The application of ML technologies has the potential to enhance best practices and standards of care for SCI management. Personalized medicine approaches using ML can tailor expectations and management strategies for individuals with SCI, considering the inherent variability in outcomes, functional prognosis, and the rehabilitation process.

Study Duration

Not specified

Participants

1983 patients with spinal cord injury

Evidence Level

Systematic Review

Key Findings

1
Machine learning shows promise in improving diagnostic accuracy through advances in MRI segmentation and classification.
2
Machine learning can contribute to acute blood pressure management, optimizing functional AISIA classification.
3
Support vector machine (SVM) models demonstrated improved accuracy compared to other ML subtypes surveyed in the context of spinal cord injury.

Research Summary

This review demonstrates ML contributions toward 1) diagnostic accuracy with advances in MRI segmentation and classification 2) acute blood pressure management for optimizing functional AISIA classification 3) quality of life clustering with predictive modeling of patient needs. Machine learning may have better efficacy in uniting datasets to create accurate algorithmic models for clinical prediction in iterative generations of modeling. Inherent variability across patients with SCI offers unique opportunity for ML and personalized medicine to drive desired outcomes and assess risks in this patient population.

Practical Implications

Enhanced Diagnostic Accuracy

ML-driven improvements in MRI segmentation and classification can lead to earlier and more accurate diagnoses of SCI, informing timely medical and surgical interventions.

Optimized Blood Pressure Management

ML can refine blood pressure targets during acute care and surgery, potentially improving neurological recovery by maintaining optimal spinal cord perfusion.

Personalized Rehabilitation

ML models can predict functional outcomes and individualize rehabilitation plans, addressing the variability in recovery among SCI patients.

Study Limitations

1
Most studies included in the analysis were retrospective in design, which can introduce bias and limit the reliability of predictive modeling.
2
The number of studies specifically focused on ML applications in SCI diagnosis and management is limited.
3
Variability in ASIA score and type for assessment and prediction should be carefully assessed and may deviate from traditional linear regression modeling for predictive algorithms.

Your Feedback

Was this summary helpful?

Back to Spinal Cord Injury