Objective: Metric-based surgical training can be used to quantify the level and progression of neurosurgical performance to optimize and monitor training progress. Here we applied innovative metrics to a physical neurosurgery trainer to explore whether these metrics differentiate between different levels of experience across different tasks.
Methods: Twenty-four participants (9 experts, 15 novices) performed 4 tasks (dissection, spatial adaptation, depth adaptation, and the A-B-A task) using the PsT1 training system. Four performance metrics (collision, precision, dissected area, and time) and 6 kinematic metrics (dispersion, path length, depth perception, velocity, acceleration, and motion smoothness) were collected.
Results: For all tasks, the execution time (t) of the experts was significantly lower than that of novices (P < 0.05). The experts performed significantly better in all but 2 of the other metrics, dispersion and sectional area, corresponding to the A-B-A task and dissection task, respectively, for which they showed a nonsignificant trend towards better performance (P = 0.052 and P = 0.076, respectively).
Conclusions: It is possible to differentiate between the skill levels of novices and experts according to parameters derived from the PsT1 platform, paving the way for the quantitative assessment of training progress using this system. During the current coronavirus disease 2019 pandemic, neurosurgical simulators that gather surgical performance metrics offer a solution to the educational needs of residents.
Keywords: Metrics; Neurosurgery; Psychomotor skills; Simulation; Technical skills; Training.
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