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Related Experiment Video

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ShPCFHNet: shepherd parallel convolutional forward harmonic net for spinal cord injury detection using CT images.

Bhagyashri Thakare1, Bhushan Chaudhari2, Madhuri Patil2

  • 1Department of Information Technology, SVKM's Institute of Technology, Dhule, Maharashtra, India. bkamankar@gmail.com.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|October 7, 2025
PubMed
Summary

A new model, ShPCFHNet, improves spinal cord injury (SCI) detection using computed tomography (CT) scans. This advanced method enhances diagnostic accuracy for predicting patient outcomes.

Keywords:
Active contour modelMagnetic resonance imagingParallel convolutional harmonic netShepard convolutional neural networkSpinal cord

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurosurgery

Background:

  • Computed Tomography (CT) is the primary imaging modality for diagnosing spinal cord injuries (SCI).
  • Accurate prediction of functional outcomes in SCI patients relies heavily on early and precise injury diagnosis.
  • Current diagnostic methods face challenges in accurately identifying initial clinical injuries.

Purpose of the Study:

  • To develop an efficient and accurate model for detecting spinal cord injuries (SCI) from CT images.
  • To enhance the prediction of functional outcomes for patients with spinal cord injuries.
  • To introduce the Shepard Parallel Convolutional Forward Harmonic Net (ShPCFHNet) for improved SCI detection.

Main Methods:

  • CT image enhancement using logarithmic transformations.
  • Spinal cord segmentation via a Dual-branch UNet with Sensitivity-Specificity Loss (SSL).
  • Disc localization using active contour models, followed by feature extraction and SCI detection with ShPCFHNet (combining ShCNN, PCNN, and Harmonic analysis).

Main Results:

  • The ShPCFHNet model achieved high performance metrics.
  • Accuracy: 91.397%
  • True Positive Rate (TPR): 92.684%
  • True Negative Rate (TNR): 90.366%

Conclusions:

  • The proposed ShPCFHNet model demonstrates significant potential for accurate SCI detection in CT imaging.
  • This AI-driven approach can aid clinicians and radiologists in improving diagnostic accuracy and functional prediction for SCI patients.
  • The integration of advanced deep learning and harmonic analysis offers a promising direction for neuroimaging research.