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Calibrated Forceps Model of Spinal Cord Compression Injury

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Published on: April 24, 2015

Severity Classification of Pediatric Spinal Cord Injuries Using Structural MRI Measures and Deep Learning: A

Zahra Sadeghi-Adl^1,2, Sara Naghizadehkashani², Devon Middleton²

¹From the Department of Electrical and Computer Engineering (Z.S.-A.), Temple University, Philadelphia, Pennsylvania zahra.sadeghiadl@jefferson.edu.

AJNR. American Journal of Neuroradiology

|April 7, 2025

Summary

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.

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Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

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.