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

• Published in: British journal of neurosurgery 2024-08, p.1-4
• Main Authors: Roche, Adam F, Redmond, Thomas, Zilani, Gulam, Healy, Vincent, Condron, Claire M
• Format: Article
• Language: English
• ISSN: 0268-8697; 1360-046X; 1360-046X
• DOI: 10.1080/02688697.2024.2391858

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. Design-based research was employed to develop the model through iterative micro-cycles, with expert evaluation from an educational and clinical team. The model was made from easy to source materials without advanced technology where sustainability, reproduction at scale and cost where significant considerations. Our model effectively simulates a retrosigmoid craniotomy and microvascular decompression of the trigeminal nerve. The model consists of two distinct parts that are made of synthetic materials. Part A is a single-use, moulded portion of the skull, while part B depicts the cerebellopontine angle and some of its internal anatomical and pathological structures crucial to carrying out all the steps to this procedure. Part A sits ergonomically flush on top of Part B, with both parts subsequently clamped to the table. As a proof of concept, we report the development and utilisation of a novel, low-cost, replicable retrosigmoid craniotomy and microvascular decompression of the trigeminal nerve simulation model.