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Augmented reality simulation framework for minimally invasive orthopedic surgery.

Antonio Cangelosi1, Giacomo Riberi2, Paolo Titolo3

  • 1Intravides SRL, Via Cristoforo Colombo, 1, Torino, 10124, Italy; DET, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, Italy; Department of Neuroscience "Rita Levi Montalcini", Università degli Studi di Torino, Corso Massimo D'Azeglio, 52, Torino, 10126, Italy.

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|March 15, 2025
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Summary

This study introduces a novel augmented reality (AR) simulation for minimally invasive surgery (MIS) training. The system enhances surgical skill development and reduces iatrogenic risks by providing accurate, real-time feedback for orthopedic procedures.

Keywords:
Augmented realityImage guided surgeryMinimally invasive surgeryOrthopedic

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Area of Science:

  • Orthopedic Surgery
  • Medical Simulation
  • Augmented Reality (AR)

Background:

  • Minimally invasive surgery (MIS) offers patient benefits but increases the risk of iatrogenic damage due to limited visibility.
  • Augmented reality (AR) can mitigate these risks by overlaying surgical information, yet implementation challenges exist.
  • A gap remains in accessible surgical skill development resources for trainees in MIS.

Purpose of the Study:

  • To present a novel simulation paradigm for orthopedic surgery training.
  • To address the challenge of mental spatial navigation in MIS procedures.
  • To demonstrate the effectiveness of an AR-based simulation for skill development and risk reduction.

Main Methods:

  • Developed a cost-effective, reproducible surgical training simulation framework.
  • Implemented a surgical gesture tracking system using a commercial depth camera.
  • Created a simulation platform with Computer Vision, X-ray simulation, and AR modules, utilizing game engines for virtual medical instrumentation.

Main Results:

  • Validated system performance through texture analysis and application accuracy evaluation.
  • Achieved high accuracy in simulating surgical device insertion (screws, K-wires), with comparative measurements showing R>0.9 (p<0.01).
  • Demonstrated a relative point-to-point accuracy of 1.02mm (SD 2.82mm), confirming the system's value for learning and AR development.

Conclusions:

  • The developed AR simulation system is a valuable tool for learning MIS procedures and for AR system developers.
  • The simulation effectively minimizes the risk of iatrogenic injury to neural and vascular structures.
  • Future work includes clinical implementation and integration of advanced AI technologies.