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Updated: Oct 6, 2026
Contrast Enhanced Ultrasound Imaging for Assessment of Spinal Cord Blood Flow in Experimental Spinal Cord Injury
Published on: May 7, 2015
Quantitative assessment of cervical spinal cord injury: Methods, clinical utility, and future directions
Shuo Sun1, Wenyu Zhang2, Yong Wang2
1School of Basic Medical Science, Naval Medical University (Second Military Medical University), Shanghai, People's Republic of China.
Study Design:
Literature review.
Objectives:
To provide a structured narrative summary of quantitative assessment methods for cervical spinal cord injury (SCI), analyze their stage-specific applicability, discuss limitations, and clarify the clinical utility and future integration of wearable sensors and artificial intelligence (AI).
Methods:
A narrative review was conducted using PubMed, Web of Science, and EBSCO to identify English-language, peer-reviewed studies published between 1967 and 2026 on quantitative assessment of traumatic and non-traumatic cervical SCI. The search covered imaging, neurological and functional evaluation, electrophysiology, biomechanics, wearable monitoring, artificial intelligence, machine learning, computer vision, and multimodal data integration. An updated PubMed search performed on May 15, 2026 identified 1,042 records. After title and abstract screening, 873 records were retained as potentially relevant, and 73 unique publications were included in the final narrative synthesis following full-text review and deduplication.
Results:
Main methods include imaging (CT, MRI/DTI, AI-image analysis), neurological scales (ASIA/ISNCSCI, SCIM, WISCI), neuroelectrophysiology (SSEP, MEP, EMG), biomechanics (sEMG and kinematics), and wearable sensors. These methods provide complementary structural, neurological, functional, and real-world activity information across acute care, rehabilitation, and long-term follow-up. Current approaches remain limited by subjectivity, poor real-time monitoring, limited quantification precision, equipment dependence, and insensitivity to early or subclinical changes. Wearable-AI integration may improve continuous monitoring, risk prediction, pressure injury prevention, and the selection of patients who may benefit from transcutaneous or epidural stimulation.
Conclusion:
Current quantitative assessment methods for cervical SCI are insufficient when used alone. A staged, multimodal strategy that combines conventional clinical assessment with wearable sensing and AI-supported analysis may improve full-process management, clinical decision-making, and prognostic assessment in cervical SCI.
