Machine Learning Introduces Electrophysiology Assessment as the Best Predictor for the Recovery Prognosis of Spinal Cord Injury Patients for Personalized Rehabilitation Approaches DOI Creative Commons

Dionysia Chrysanthakopoulou,

Charalampos Matzaroglou,

Eftychia Trachani

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4578 - 4578

Published: April 21, 2025

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship variations somatosensory (SSEPs) ASIA scores, especially the early stages of SCI. Machine learning’s (ML’s) increasing importance medicine is driven by growing availability health data improved algorithms. It enables creation models disease diagnosis, progression prediction, personalized treatment, healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, facilitate medicine. meticulous analysis medical crucial timely identification, leading to effective symptom management appropriate treatment. This study applies artificial intelligence identify predictors SCI progression, measured disability index, impairment scale (AIS), final motor recovery. We aim clarify prognostic role electrophysiological testing (SSEPs, MEPs, nerve conduction (NCSs)) analyzed from database 123 records. developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees neural network approaches, predict Our evaluation showed SEP accuracies 90% recovery prediction 80% AIS determination, comparable full electrophysiology 93% 89%, respectively, generally superior results compared MEP NCS results. emerged best predictors, comprehensive assessment, improving accuracy clinical findings alone. An when available, increased overall (from maximum 75%) and, score 89% 66%). Further validation needed larger dataset. Future research should validate sensory assessment less expensive, portable, simpler alternative other tests more than assessments, like AIS, biomarker SCI, planning.

Language: Английский

Machine Learning Introduces Electrophysiology Assessment as the Best Predictor for the Recovery Prognosis of Spinal Cord Injury Patients for Personalized Rehabilitation Approaches DOI Creative Commons

Dionysia Chrysanthakopoulou,

Charalampos Matzaroglou,

Eftychia Trachani

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4578 - 4578

Published: April 21, 2025

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship variations somatosensory (SSEPs) ASIA scores, especially the early stages of SCI. Machine learning’s (ML’s) increasing importance medicine is driven by growing availability health data improved algorithms. It enables creation models disease diagnosis, progression prediction, personalized treatment, healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, facilitate medicine. meticulous analysis medical crucial timely identification, leading to effective symptom management appropriate treatment. This study applies artificial intelligence identify predictors SCI progression, measured disability index, impairment scale (AIS), final motor recovery. We aim clarify prognostic role electrophysiological testing (SSEPs, MEPs, nerve conduction (NCSs)) analyzed from database 123 records. developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees neural network approaches, predict Our evaluation showed SEP accuracies 90% recovery prediction 80% AIS determination, comparable full electrophysiology 93% 89%, respectively, generally superior results compared MEP NCS results. emerged best predictors, comprehensive assessment, improving accuracy clinical findings alone. An when available, increased overall (from maximum 75%) and, score 89% 66%). Further validation needed larger dataset. Future research should validate sensory assessment less expensive, portable, simpler alternative other tests more than assessments, like AIS, biomarker SCI, planning.

Language: Английский

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