RESUMO
Goal: Conventionally, a surgeon's skill is assessed through visual observation by experts and by tracking patient outcomes. These techniques are very subjective and demands enormous time and effort. Hence, the aim of this study is to construct a framework for automated objective assessment of micro-neurosurgical skill. Methods: A mask region-based convolution neural network (RCNN) is trained to identify and localize instances of surgical instruments from the recorded neurosurgery videos. Then the tool motion and tool handling metrics are computed by tracking the detected instrument locations through time. Microscope adjustment patterns are also investigated via the proposed time based metrics.Results: This study highlights the metrics that could potentially emphasize the variance in expertise between a veteran and a novice. These variations include an expert exhibiting a lower velocity, lower acceleration, lower jerks, reduced path length, higher normalized angular displacement, increased bi-manual handling, shorter idle time and smaller inter tool-tip distances while handling tools accompanied with frequent microscope adjustments and reduced maximum and median intervals between adjustments when compared to a novice. Conclusions: The developed vision based framework has proven to be a reliable method to assess the degree of surgical skill objectively and offer prompt and precise feedback to the neurosurgeons.
