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Fully immersive virtual reality for skull-base surgery: surgical training and beyond
Adnan Munawar1, Zhaoshuo Li2, Nimesh Nagururu3
1Johns Hopkins University, Baltimore, MD, 21218, USA. amunawa2@jh.edu.
Summary
A new virtual reality (VR) system for skull-base surgery training offers a cost-effective, immersive alternative to cadavers. The Fully Immersive Virtual Reality System (FIVRS) collects detailed data for skill assessment.
Area of Science:
- Neurosurgery
- Medical Simulation
- Virtual Reality Technology
Background:
- Cadaveric training for skull-base surgery is expensive and limited in scalability.
- Objective skill assessment in surgery is challenging with traditional methods.
- Virtual reality (VR) offers a promising avenue for surgical training and assessment.
Purpose of the Study:
- To introduce the Fully Immersive Virtual Reality System (FIVRS) for skull-base surgery training.
- To combine advanced surgical simulation software with high-fidelity hardware for realistic surgical practice.
- To enable objective skill assessment through digitized surgical data collection.
Main Methods:
- FIVRS integrates realistic bone ablation using advanced rendering and drilling algorithms.
- A head-mounted display mimics surgical microscope ergonomics for enhanced immersion.
- Multi-modal data, including eye gaze, motion, force, and video, are recorded for post-analysis.
- A user-friendly interface is incorporated to reduce the learning curve.
Main Results:
- Preliminary user study data from surgeons of varying expertise levels show FIVRS can differentiate skill levels.
- The system's ability to collect objective metrics shows promise for future automatic skill assessment research.
- Participants provided positive informal feedback regarding the system's intuitiveness and immersiveness.
Conclusions:
- FIVRS is a fully immersive VR system designed for skull-base surgery.
- The system combines realistic simulation software with modern hardware for an enhanced training experience.
- FIVRS is open-source and outputs comprehensive data in standard formats.

