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Severity Classification of Pediatric Spinal Cord Injuries Using Structural MRI Measures and Deep Learning: A
Zahra Sadeghi-Adl1,2, Sara Naghizadehkashani2, Devon Middleton2
1From the Department of Electrical and Computer Engineering (Z.S.-A.), Temple University, Philadelphia, Pennsylvania zahra.sadeghiadl@jefferson.edu.
Insights
Deep learning accurately detects pediatric spinal cord injury (SCI) and its severity using MRI-based structural measurements. This advanced approach improves diagnostic accuracy for better patient care in pediatric SCI management.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Pediatric spinal cord injury (SCI) diagnosis is challenging due to difficulties in clinical assessment.
- Accurate evaluation of spinal cord structural changes is crucial for effective treatment planning.
Purpose of the Study:
- To evaluate structural characteristics (cross-sectional area, AP width, RL width) in pediatric SCI patients.
- To compare these measures between typically developing (TD) and SCI pediatric participants.
- To employ deep learning for SCI detection and severity assessment.
Main Methods:
- Sixty-one pediatric participants (20 SCI, 41 TD) underwent 3T MRI scans.
- T2-weighted MRI scans were used to measure spinal cord dimensions (CSA, AP, RL widths) at all vertebral levels.
- Deep convolutional neural networks (CNNs) were trained to classify participants and determine injury severity (AIS category).
Main Results:
- Significant structural differences (p<0.05) in CSA, AP, and RL widths were observed between SCI and TD groups.
- CNN models achieved 96.59% accuracy in distinguishing SCI from TD participants.
- AIS category classification was achieved with 94.92% accuracy.
Conclusions:
- Integrating structural imaging measures with deep learning effectively classifies pediatric SCI and assesses severity.
- Deep learning models demonstrate superior diagnostic accuracy compared to traditional machine learning.
- This approach holds potential for improving pediatric SCI patient care.
Background And Purpose:
Spinal cord injury (SCI) in the pediatric population presents a unique challenge in diagnosis and prognosis due to the complexity of performing clinical assessments on children. Accurate evaluation of structural changes in the spinal cord is essential for effective treatment planning. This study aims to evaluate structural characteristics in pediatric patients with SCI by comparing cross-sectional area (CSA), anterior-posterior (AP) width, and right-left (RL) width across all vertebral levels of the spinal cord between typically developing (TD) and participants with SCI. We employed deep learning techniques to utilize these measures for detecting SCI cases and determining their injury severity.
Materials And Methods:
Sixty-one pediatric participants (ages 6-18), including 20 with chronic SCI and 41 TD, were enrolled and scanned by using a 3T MRI scanner. All SCI participants underwent the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) test to assess their neurologic function and determine their American Spinal Injury Association (ASIA) Impairment Scale (AIS) category. T2-weighted MRI scans were utilized to measure CSA, AP width, and RL widths along the entire cervical and thoracic cord. These measures were automatically extracted at every vertebral level of the spinal cord by using the spinal cord toolbox. Deep convolutional neural networks (CNNs) were utilized to classify participants into SCI or TD groups and determine their AIS classification based on structural parameters and demographic factors such as age and height.
Results:
Significant differences (P < .05) were found in CSA, AP width, and RL width between SCI and TD participants, indicating notable structural alterations due to SCI. The CNN-based models demonstrated high performance, achieving 96.59% accuracy in distinguishing SCI from TD participants. Furthermore, the models determined AIS category classification with 94.92% accuracy.
Conclusions:
The study demonstrates the effectiveness of integrating cross-sectional structural imaging measures with deep learning methods for classification and severity assessment of pediatric SCI. The deep learning approach outperforms traditional machine learning models in diagnostic accuracy, offering potential improvements in patient care in pediatric SCI management.
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