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Related Experiment Video

Updated: Nov 14, 2025

Author Spotlight: Development and Application of a Novel Suture Technique for Annular Fibrosus Repair in Percutaneous Transforaminal Endoscopic Discectomy
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Author Spotlight: Development and Application of a Novel Suture Technique for Annular Fibrosus Repair in Percutaneous Transforaminal Endoscopic Discectomy

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Nerve recognition in percutaneous transforaminal endoscopic discectomy using convolutional neural network.

Peng Cui1, Tao Shu2, Jun Lei3

  • 1Biomedical Information Engineering Lab, The University of Aizu, Aizu-Wakamatsu City, Fukushima, 965-8580, Japan.

Medical Physics
|March 8, 2021
PubMed
Summary

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A new computer-aided detection system (CADS) using convolutional neural networks (CNNs) accurately identifies nerve and dura mater during percutaneous transforaminal endoscopic discectomy (PTED). This AI tool enhances surgeon performance in tissue recognition during minimally invasive procedures.

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Percutaneous transforaminal endoscopic discectomy (PTED) is a prevalent minimally invasive surgical technique.
  • Accurate intraoperative identification of neural structures like nerves and dura mater is critical during PTED.

Purpose of the Study:

  • To develop and evaluate a computer-aided detection system (CADS) utilizing convolutional neural networks (CNNs).
  • The CADS aims to automatically recognize nerve and dura mater images during PTED surgery.

Main Methods:

  • Surgical videos from 65 patients undergoing PTED for lumbar disc herniation were collected.
  • Videos were converted to images, with datasets created for training, validation, and testing (10,454 training/validation images, 12,000 testing images).
Keywords:
computer-aided detectionconvolutional neural networkpercutaneous transformational endoscopic discectomytissue recognize

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  • A CNN-based CADS was developed to analyze these images.

    • Main Results:

    • The CADS achieved high performance metrics: 90.90% sensitivity, 93.68% specificity, and 92.29% accuracy.
    • The system demonstrated consistent performance across different patients in the test dataset.
    • While outperformed by a specialist spinal endoscopist, the CADS surpassed general surgeons and improved their performance to near specialist levels when used as an aid.

    Conclusions:

    • Convolutional neural networks (CNNs) are effective for recognizing nerve and dura mater images in PTED.
    • The developed CADS can assist general surgeons in improving their intraoperative tissue recognition capabilities.