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Abstract #5103

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

Zahra Sadeghi Adl1,2, Sara Naghizadehkashani 1, Laura Krisa3, Devon Middleton 1, Mahdi Alizadeh 1, Adam Flanders1, Scott H. Faro1, and Feroze B. Mohamed 1
1Radiology, Thomas Jefferson University, Philadelphia, PA, United States, 2Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, United States, 3Department of Physical Therapy, Thomas Jefferson University, Philadelphia, PA, United States

Synopsis

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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Keywords