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This is a Quantitative Study study.

September 17, 2025

Deep Learning-Driven Prediction of Pediatric Spinal Cord Injury Severity Using Comprehensive Structural MRI Analysis

ZAZahra Sadeghi AdlThomas Jefferson University SNSara NaghizadehkashaniUniversity of Maryland, Baltimore LKLaura KrisaThomas Jefferson University

Key Points

The model achieved 96.6% accuracy in classifying spinal cord injury severity.
Using MRI measurements, significant structural differences were identified between injury and typical development groups.
A convolutional neural network trained on spinal MRI data provided high predictive accuracy for ASIA categories.
Identifying structural MRI biomarkers can refine injury assessment and improve pediatric spinal cord injury management.

Abstract

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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