Developing a high fidelity, low cost simulation model for retrosigmoid craniotomy and microvascular decompression of the trigeminal nerve.

Background: Trigeminal neuralgia is a very painful condition that may require a surgical approach as treatment, which is typically retrosigmoid craniotomy followed by microvascular decompression. Due to the limited margin for error when operating in the small triangular window of the cerebellopontine angle and the infrequency of this condition, the operating room can present a difficult learning environment for surgical trainees. Our aim is to create a synthetic, low-cost, high-fidelity, and largely reusable simulation model that will enable neurosurgical trainees to practice these procedural steps in a safe learning environment.