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Intraoperative Ultrasound in Spinal Surgery
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Spinal tissue identification using a Forward-oriented endoscopic ultrasound technique.

Jiaqi Yao1, Yiwei Xiang1, Chang Jiang2

  • 1School of Information Science and Technology, ShanghaiTech University, No. 393 Middle Huaxia Road, Pudong New District, Shanghai, 201210 China.

Biomedical Engineering Letters
|January 9, 2025
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Summary
This summary is machine-generated.

This study introduces an ultrasound endoscopic system for real-time spinal tissue identification during minimally invasive spine surgery (MISS). Deep learning models, particularly the Vision Transformer (ViT), achieved high accuracy, enhancing surgical safety.

Keywords:
Deep learningMinimally invasive spine surgerySpinal tissue identificationUltrasound tissue characterization

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Area of Science:

  • Biomedical Engineering
  • Surgical Technology
  • Medical Imaging

Background:

  • Optical endoscopes have limited imaging depth, increasing risks during minimally invasive spine surgery (MISS).
  • Accurate identification of spinal soft tissues is crucial for preventing critical tissue damage.

Purpose of the Study:

  • To develop and evaluate a forward-oriented ultrasound endoscopic system for automatic spinal soft tissue identification.
  • To improve the accuracy and effectiveness of intraoperative tissue recognition in MISS.

Main Methods:

  • Collected 758 ex-vivo ovine spinal soft tissue samples (spinal cord, nucleus pulposus, adipose tissue, nerve root).
  • Applied conventional methods (GLCM, EWT, VMD) and deep learning models (DenseNet, 1D ViT).
  • Evaluated model performance based on accuracy, specificity, and inference latency.

Main Results:

  • Deep learning methods (DenseNet, ViT) surpassed conventional methods, achieving >95% accuracy.
  • The Vision Transformer (ViT) model demonstrated 98.31% accuracy and 99.2% specificity.
  • ViT achieved a low inference latency of 0.0025 seconds, enabling real-time application.

Conclusions:

  • The forward-oriented ultrasound endoscopic system is feasible for real-time intraoperative recognition of critical spinal tissues.
  • This technology enhances precision and safety in minimally invasive spine surgery.
  • Deep learning, especially ViT, shows significant potential for improving spinal tissue identification in surgical settings.