Training the next generation: a blueprint for simulation-based microsurgical education in neurosurgery

Area of Science:

• Neurosurgery
• Medical Simulation
• Surgical Training

Background:

• Neurosurgical training faces challenges due to reduced surgical caseloads and limited hands-on practice.
• Existing haptic simulators are often expensive, lack realism, and are not standardized, hindering widespread adoption.
• There is a need for accessible, realistic, and reproducible simulators for complex neurosurgical procedures.

Purpose of the Study:

• To develop and evaluate a highly realistic, low-cost, and fully replicable microsurgical simulator.
• To provide a tool for repetitive training in complex cranial procedures like dissections, aneurysm clipping, and tumor resections.
• To assess the simulator's educational efficacy and transferability of skills.

Main Methods:

• Developed a simulator using high-resolution anatomical reconstructions and additive manufacturing with accessible materials.
• Utilized freeware for the entire development process and provided detailed blueprints for replication.
• Evaluated the simulator with 22 participants of varying experience levels using objective assessment tools.

Main Results:

• The simulator was produced at minimal cost without specialized infrastructure.
• Participants across all experience levels highly rated realism, usability, and educational value (mean Likert score 4.9/5).
• Objective assessments showed strong construct validity and significant technical skill improvement, especially in novices.

Conclusions:

• Anatomically accurate, affordable neurosurgical simulators can be created using freeware and accessible materials.
• The simulator facilitates realistic, hands-on practice for diverse procedures, including patient-specific simulations.
• This development offers a practical, scalable solution to enhance standardized neurosurgical curricula and training limitations.