Virtual Reality and 3-Dimensional-Printed Model-Based Craniotomy Skills Curriculum for Under-Resourced Healthcare...

Low- and middle-income countries (LMICs) represent fertile ground for the development of neurosurgical simulation models using virtual reality (VR) and 3-dimensional (3D) printing, particularly because cadaver use is often limited in LMICs due to financial, cultural, and/or legal constraints. This observational study describes the development and validation of a craniotomy training curriculum incorporating VR and 3D-printed models with potential utility in LMICs.

The curriculum was piloted by 40 neurosurgical residents and attending neurosurgeons in a LMIC. Twenty participants trained using a VR model and subsequently performed craniotomies on a 3D-printed model. The remaining 20 participants performed craniotomies on the 3D-printed model without VR training. Performance metrics included the need for size correction, correct osteotomy order, time needed, and craniometric landmark use. Participants completed a questionnaire assessing curriculum fidelity.

Fourteen VR-trained participants (70%) performed craniotomies that did not require size correction, compared with 4 non-VR-trained participants (20%, 95% CI 23.3%-76.7%, P < .01). VR-trained participants performed the osteotomies in the correct order more frequently than non-VR-trained participants (VR group, 18 [90%]; non-VR group, 6 [30%]; P < .01). Seventeen VR-trained participants (85%) finished the craniotomies by the end of the session, compared with 8 non-VR-trained participants (40%, 95% CI 18.4%-71.6%, P < .01). Nineteen VR-trained participants (95%) used craniometric landmarks during the physical surgical simulation, compared with 11 non-VR-trained participants (55%, 95% CI 16.2%-63.8%, P < .01). Thirty-eight participants (95%) felt the model had enough anatomic fidelity for neurosurgical training.

The curriculum described represents a viable, cost-effective alternative to cadaveric neurosurgical training that may improve access to surgical simulation in LMICs.