Keywords: Diagnosis/Prediction, Analysis/Processing
Motivation: Diagnosing pediatric spinal cord injury (SCI) is challenging due to limitations in current clinical assessments.
Goal(s): Our study investigates how structural changes in the pediatric spinal cord can predict SCI severity, using cross-sectional measurements and deep learning for ASIA Impairment Scale classification.
Approach: We analyzed spinal MRI data from 61 pediatric subjects, measuring cross-sectional area, anterior-posterior, and right-left widths across vertebral levels. A convolutional neural network was trained on these structural features, combined with age and height.
Results: The model achieved 96.6% accuracy in classifying SCI and 94.9% accuracy in predicting ASIA categories, identifying significant structural differences between SCI and TD groups.
Impact: This research identifies structural MRI biomarkers for pediatric SCI severity, offering a precise tool for assessing injury severity. The approach offers clinicians a potential tool for refined injury assessment and sets a foundation for further advancements in pediatric SCI management.
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