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Related Experiment Video

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Augmented Reality Surgical Navigation System Integrated with Deep Learning.

Shin-Yan Chiou1,2,3, Li-Sheng Liu1,4, Chia-Wei Lee1

  • 1Department of Electrical Engineering, College of Engineering, Chang Gung University, Kwei-Shan, Taoyuan 333, Taiwan.

Bioengineering (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost, high-accuracy augmented reality (AR) surgical navigation system. The system provides intuitive guidance for surgeons, improving accuracy in procedures like extra-ventricular drainage surgery.

Keywords:
EVD surgeryaugmented realityautomatic scanningdeep learningmixed realityneurosurgerysurgical navigation

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Area of Science:

  • Medical Technology
  • Surgical Navigation
  • Augmented Reality

Background:

  • Current optical surgical navigation systems often use external screens, leading to non-intuitive spatial information and distractions.
  • Existing augmented reality (AR) surgical navigation research primarily focuses on visual aids, neglecting practical guidance and often sacrificing system stability and accuracy.
  • Traditional optical navigation systems can be expensive and less stable.

Purpose of the Study:

  • To develop a cost-effective, stable, and accurate AR surgical navigation system.
  • To provide intuitive, real-time surgical guidance for target point, entry point, and trajectory.
  • To enhance surgical precision and reduce cognitive load during procedures.

Main Methods:

  • An AR surgical navigation system based on image positioning was developed.
  • A "virtual object automatic scanning" method was employed to achieve high positional accuracy (1 ± 0.1 mm).
  • A deep learning U-Net segmentation network was integrated for automatic hydrocephalus identification.

Main Results:

  • The system demonstrated high accuracy, stability, and low cost.
  • Clinical trials for extra-ventricular drainage surgery confirmed the system's benefits.
  • The deep learning model achieved high recognition accuracy (99.93%), sensitivity (93.85%), and specificity (95.73%) for hydrocephalus detection.

Conclusions:

  • The proposed AR surgical navigation system offers significant advantages over existing methods.
  • The system provides intuitive guidance, improving surgical accuracy and potentially patient outcomes.
  • The integration of advanced imaging and deep learning enhances diagnostic capabilities within the surgical workflow.