2057 Virtual Reality and 3D-Printed Model-Based Craniotomy Skills Curriculum for Under-Resourced Healthcare Systems

INTRODUCTION: Often benefiting last from medical innovations, low- and middle-income countries (LMICs) represent fertile ground for the development of neurosurgical simulation models using virtual reality (VR) and 3D printing. This is because the use of cadavers, the current “gold standard” of neurosurgical simulation, is limited in LMICs due to financial, cultural, and/or legal constraints. METHODS: The curriculum was piloted by 40 neurosurgical residents and young attendings in a LMIC. 20 participants trained using a VR model and subsequently performed a craniotomy on a 3D printed model. The remaining 20 participants performed a craniotomy on the 3D printed model immediately without prior VR training. Performance metrics collected for both groups during the simulation with the physical model included the need for craniotomy size correction, correct osteotomy order, time needed to complete the craniotomy, and correct craniometric landmark use. Participants also completed a questionnaire assessing curriculum fidelity and prior experience with surgical simulation training and VR. RESULTS: 70% of VR-trained participants performed a craniotomy that did not require size correction, compared to 20% of the remaining participants (p<0.01). VR-trained participants more frequently performed the osteotomies in the correct order (VR=90%, no VR=30%) (p<0.01). 85% of VR-trained participants finished the craniotomy by the end of the session, compared 40% of the remaining participants (p<0.01). 95% of VR-trained participants used craniometric landmarks during the physical surgical simulation, compared to 55% of their counterparts (p<0.01). 95% of participants felt the model had enough anatomical fidelity for neurosurgical training. CONCLUSIONS: The curriculum developed by the authors represents a viable, cost-effective alternative to cadaveric neurosurgical training that may improve access to surgical simulation in LMICs.