Intelligent Integration of Assessment Tools for Specialized Prognosis in Spinal Cord Injuries: A scoping review

Abstract

Abstract Spinal cord injury is a life-threatening condition resulting from spinal cord trauma, leading to paralysis, loss of sensation, bowel and bladder control. Accurate assessment tools are crucial for diagnosing and treating spinal cord injuries, and various scales have been developed for this purpose. Additionally, electrophysiological measures, including somatosensory evoked potentials, motor evoked potentials, and nerve conduction studies, can aid in patient stratification. Recent developments in spinal cord injury assessment have shown promise, particularly with the use of advanced imaging techniques and artificial intelligence. Neuroimaging and molecular biomarkers combined with electrophysiological measures, promise to predict outcomes and guide treatment decisions. Machine learning and Artificial intelligence have revolutionized the healthcare industry, including the field of spinal cord injuries, as they can facilitate personalized medicine by accurately predicting. Challenges remain in validating machine learning models and ensuring they are safe and effective for clinical use. Quality data and expertise are crucial for accurately interpreting and applying machine learning results in spinal cord injury management. Moreover, due to artificial intelligence entering healthcare to assist in processing data, electrophysiology can eventually meet the high-quality information it can provide, as it is easier to analyze data recordings from somatosensory evoked potentials and other electrophysiologic measures. Summing up, the integration of advanced imaging techniques, biomarkers, and machine learning leading to maximizing the use and importance of electrophysiology as far as the information it can reveal, has the potential to revolutionize the diagnosis, prognosis, and treatment of spinal cord injuries, leading to improved patient outcomes and personalized care.