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Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator

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Research has clearly shown the benefits of surgical simulators to train laparoscopic motor skills required for positive patient outcomes. We have developed the Virtual Basic Laparoscopic Skill Trainer (VBLaST) that simulates tasks from the Fundamentals of Laparoscopic Surgery (FLS) curriculum. This study aims to show convergent validity of the VBLaST pattern cutting module via the CUSUM method to quantify learning curves along with motor skill transfer from simulation environments to ex vivo tissue samples.


18 medical students at the University at Buffalo, with no prior laparoscopic surgical skills, were placed into the control, FLS training, or VBLaST training groups. Each training group performed pattern cutting trials for 12 consecutive days on their respective simulation trainers. Following a 2-week break period, the trained students performed three pattern cutting trials on each simulation platform to measure skill retention. All subjects then performed one pattern cutting task on ex vivo cadaveric peritoneal tissue. FLS and VBLaST pattern cutting scores, CUSUM scores, and transfer task completion times were reported.


Results indicate that the FLS and VBLaST trained groups have significantly higher task performance scores than the control group in both the VBLaST and FLS environments (p < 0.05). Learning curve results indicate that three out of seven FLS training subjects and four out of six VBLaST training subjects achieved the “senior” performance level. Furthermore, both the FLS and VBLaST trained groups had significantly lower transfer task completion times on ex vivo peritoneal tissue models (p < 0.05).


We characterized task performance scores for trained VBLaST and FLS subjects via CUSUM analysis of the learning curves and showed evidence that both groups have significant improvements in surgical motor skill. Furthermore, we showed that learned surgical skills in the FLS and VBLaST environments transfer not only to the different simulation environments, but also to ex vivo tissue models.

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This work is supported by NIBIB 1R01EB014305, NHLBI 1R01HL119248, and NCI 1R01CA197491 Grants awarded to Suvranu De. The authors would like to thank the medical student subjects and their dedication for this study. The authors would also like to thank the anatomical gift program and the gross anatomy lab at University of Buffalo for their support regarding the ex vivo cadaveric samples.

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Correspondence to Suvranu De.

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Drs. Arun Nemani, Woojin Ahn, Clairice Cooper, Steven Schwaitzberg, and Suvranu De have no conflict of interest or financial ties to disclose.

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Nemani, A., Ahn, W., Cooper, C. et al. Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator. Surg Endosc 32, 1265–1272 (2018).

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