Nonlinear Analysis Quantifies Learning in Robot-Assisted Laparoscopic Surgery

To determine the proficiency of surgical skills in robot-assisted laparoscopy is important to identify the learning pattern. Traditional measurements, such as time to task completion (TTC), does not quantify accuracy and quality of learning. The aim of this study was to find a better measure to evaluate quality of learning. Lyapunov Exponent (LyE) is a nonlinear measure which can determine the consistency of task performance. Consistency is assessed by measuring the amount of deviation in movement paths of the surgical instrument tips. Eight right-handed medical students participated in training sessions on two consecutive days. In both sessions each participant passed a surgical needle through ten points along an inanimate material using the da Vinci Surgical System (Intuitive Surgical, Inc.). Subjects also performed the same task before and after training. Measurements such as TTC, total traveling distance and speed were also calculated. Paired t-tests were used to compare pre- and post-training for all measurements. Our results indicated that the LyE values showed significantly less deviated trajectories of the instrument tips after training. The movement path showed more consistent cyclic pattern from pre- (Fig.1) to post-training (Fig.2). TTC also decreased significantly with no differences in the total distance traveled. Our results showed that trainees could make faster movement with improved consistency after training. Nonlinear analysis tools provide surgeons with better accuracy evaluation and visualization of the surgical performance. Therefore, evaluation of proficiency in robot-assisted laparoscopic surgery could be improved by combining both nonlinear analysis and traditional measurements.


Session: Poster

Program Number: P391

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