Dominant hand, non-dominant hand, or both? The effect of pre-training in hand-eye coordination upon the learning curve of laparoscopic intra-corporeal knot tying

Abstract

BackgroundTraining of basic laparoscopic psychomotor skills improves both acquisition and retention of more advanced laparoscopic tasks, such as laparoscopic intra-corporeal knot tying (LICK). This randomized controlled trial (RCT) was performed to evaluate the effect of different pre-training programs in hand-eye coordination (HEC) upon the learning curve of LICK.ResultsThe study was performed in a private center in Asunción, Paraguay, by 60 residents/specialists in gynaecology with no experience in laparoscopic surgery. Participants were allocated in three groups. In phase 1, a baseline test was performed (T 1, three repetitions). In phase 2, participants underwent different training programs for HEC (60 repetitions): G1 with both the dominant hand (DH) and the non-dominant hand (NDH), G2 with the DH only, G3 none. In phase 3, a post HEC/pre LICK training test was performed (T 2, three repetitions). In phase 4, participants underwent a standardized training program for LICK (60 repetitions). In phase 5, a final test was performed (T 3, three repetitions). The score was based on the time taken for task completion system. The scores were plotted and non-linear regression models were used to fit the learning curves to one- and two-phase exponential decay models for each participant (individual curves) and for each group (group curves). For both HEC and LICK, the group learning curves fitted better to the two-phase exponential decay model. For HEC with the DH, G1 and G2 started from a similar point, but G1 reached a lower plateau at a higher speed. In G1, the DH curve started from a lower point than the NDH curve, but both curves reached a similar plateau at comparable speeds. For LICK, all groups started from a similar point, but immediately after HEC training and before LICK training, G1 scored better than the others. All groups reached a similar plateau but with a different decay, G1 reaching this plateau faster than the others groups.ConclusionsThis study demonstrates that pre-training in HEC with both the DH and the NDH shortens the LICK learning curve.

A Haptic Laparoscopic Trainer Based on Affine Velocity Analysis: Engineering and Preliminary Results

“…The outcomes of the aforementioned validation protocols showed that the simulator with usual low-fidelity simulators. As training solutions outside the operation room need to be engineered for the initial training period of surgeons [5,60], and as this simulator main aim is the learning of basic laparoscopic skills, we could assume that this simulator might be a mean to complement the existing contents of basic surgical education. This perspective needs, however, to be confirmed by investigating the implementation of this simulator in an early training stage along with the study of the impact of haptic feedback on learning.…”

“…To better analyze and understand these results, we should consider that this was a bi-manual task, while the first exercise only involved one hand. Surgery skills required the coordination of the two hands [56] and the ability of using both hands is essential in laparoscopic surgery [7,57] and in open surgery [5]. [58], which can also affect the outcomes.…”

“…We designed all vir-tual objects with the 3DS Max software 2016 [4] . We finally collected and processed performance data (affine velocity, motion duration, numbers of collisions) through Matlab R R2015a (Mathworks) [5] . ***Insert figure 2 around here*** Affine velocity is a metric that takes the geometry of the trajectory into account, as well as its dynamics.…”

“…Affine velocity was used instead of the velocity itself because it provides valuable information about the quality and smoothness of the trajectory. Data from each training session (tra- [4] see http://www.autodesk.fr/products/3ds-max/overview [5] see https://fr.mathworks.com jectories, moments, and numbers of collisions, right/left-hand distributions) were collected through CHAI3D into a file and post-processed using Matlab to evaluate the trainees' performance. In future versions, these assessment algorithms will be integrated into the simulator software.…”

“…Therefore, a different skill set, e.g. involving spatial abilities [4], bi-manual coordination [5], and hand-eye coordination [6], is essential in MIS practice. At the early stages of MIS learning, the relevance of these skills is increased [7].…”