High Simulation Training System for Spinal Full-Endoscopic Surgery, Based on the Combination of VR/AR and Magneto-Optical Navigation Technology.

Abstract

Objective: Spinal full-endoscopic surgery is a challenging technique with a steep learning curve, limited by inadequate training models and the shortcomings of cadaver-based training. To address this, we propose a high-simulation training system using VR/AR and magneto-optical navigation technology to enhance skill development and reduce the learning curve.

Methods: A new simulation training system for spinal full-endoscopic surgery was established, which was conducted by using the data of Chinese Digital Human with medical image parameters for the three-dimensional (3D) reconstruction, as well as integrating the technical advantages of VR/AR, 3D printing, and magneto-optical navigation technology.

Results: Based on the original dataset of Chinese digital humans and clinical medical imaging processing, the data model was obtained and then the 3D printing engineering model was created. The simulation perspective filming technology, joint process shaping and cutting technology, and tube placement technology have been constructed, based on the combination of VR/AR under optical navigation. Based on electromagnetic tracking, a microscopic anatomical simulation using preorder optical navigation has been designed. Finally, a physical simulation model based on the clinical reality of flocculent behavior was constructed. As a simulator for spinal endoscopic surgery training, it was tested during an advanced endoscopic training course. Moreover, 17 out of 20 novices (85%) met the surgical standards by the end of the final simulation training session.

Conclusion: We present a high simulation training system based on the combination of VR/AR and magneto-optical navigation for spinal full-endoscopic surgery. The model may be used by surgeons starting with spinal endoscopy and should be considered a comparable and sufficiently realistic tool to train key operation steps to reduce the learning curve.