Laparoscopic Motor Learning and Workspace Exploration

摘要

Background: Laparoscopic surgery requires operators to learn novel complex movement patterns. However, our understanding of how best to train surgeons’ motor skills is inadequate and research is needed to determine optimal laparoscopic training regimes. This difficulty is confounded by variables inherent in surgical practice – e.g. the increasing prevalence of morbidly obese patients presents additional challenges related to restriction of movement due to abdominal wall resistance and reduced intra-abdominal space. The aim of this study was to assess learning of a surgery related task in constrained and unconstrained conditions using a novel system linking a commercially available robotic arm with specialised software creating the novel kinematic assessment tool (Omni-KAT). Methods: We created an experimental tool that records motor performance by linking a commercially available robotic arm with specialised software that presents visual stimuli and objectively measures movement outcome (kinematics). Participants were given the task of generating aiming movements along a horizontal plane to move a visual cursor on a vertical screen. One group received training that constrained movements to the correct plane whilst the other group was unconstrained and could explore the entire ‘action space’. Results: The tool successfully generated the requisite force fields and precisely recorded the aiming movements. Consistent with predictions from structural learning theory, the unconstrained group produced better performance after training as indexed by movement duration (p < .05). Conclusion: The data showed improved performance for participants who explored the entire action space, highlighting the importance of learning the full dynamics of laparoscopic instruments. These findings, alongside the development of the Omni-KAT, open up exciting prospects for better understanding of the learning processes behind surgical training and investigating ways in which learning can be optimised.