Time-efficient laparoscopic skills assessment using an augmented-reality simulator
- PMID: 18347859
- DOI: 10.1007/s00464-008-9844-9
Time-efficient laparoscopic skills assessment using an augmented-reality simulator
Abstract
Background: Computer-based, virtual-reality laparoscopic surgical simulators have several advantages over traditional video trainers. One of these advantages is that performance can be evaluated using unique computer-derived metrics, which can be digitally archived for analysis at a time convenient to instructors. This study sought to determine whether the computer-derived metrics for a unique hybrid simulator correlated with laparoscopic surgical skill.
Methods: For this study, 24 medical students (3rd year), 19 surgical residents (postgraduate years 1-5), and 3 attending surgeons were invited to perform four different tasks three times in a hybrid laparoscopic trainer (ProMIS). Instruction with minimal supervision occurred at a time convenient to each subject. The four tasks in order of complexity were laparoscopic orientation, object positioning, sharp dissection, and intracorporeal knot tying. The metrics automatically recorded were time, path length, and smoothness. The laparoscopic operative experience for each user was quantified using case logs.
Results: A statistically significant correlation was observed between experience and performance for all three metrics for tasks 2 to 4 (p < 0.01). Smoothness was the only metric that correlated with the laparoscopic orientation task. Within tasks, time and smoothness correlated much more strongly with experience and to a similar degree. The strongest correlation was observed for the knot-tying task (r(2) = 0.60 for time and 0.59 smoothness).
Conclusions: The computer-derived metrics measured by the hybrid trainer correlate with laparoscopic experience. These metrics are automatically calculated and stored. This may make skills assessment and training a more time-efficient endeavor for instructors and trainees alike. Further study is necessary to determine whether specific metrics are better indicators of actual skill.
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