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Face and construct validation of a virtual peg transfer simulator

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The Fundamentals of Laparoscopic Surgery (FLS) trainer box is now established as a standard for evaluating minimally invasive surgical skills. A particularly simple task in this trainer box is the peg transfer task which is aimed at testing the surgeon’s bimanual dexterity, hand–eye coordination, speed, and precision. The Virtual Basic Laparoscopic Skill Trainer (VBLaST©) is a virtual version of the FLS tasks which allows automatic scoring and real-time, subjective quantification of performance without the need of a human proctor. In this article we report validation studies of the VBLaST© peg transfer (VBLaST-PT©) simulator.


Thirty-five subjects with medical background were divided into two groups: experts (PGY 4–5, fellows, and practicing surgeons) and novices (PGY 1–3). The subjects were asked to perform the peg transfer task on both the FLS trainer box and the VBLaST-PT© simulator; their performance was evaluated based on established metrics of error and time. A new length of trajectory (LOT) metric has also been introduced for offline analysis. A questionnaire was used to rate the realism of the virtual system on a 5-point Likert scale.


Preliminary face validation of the VBLaST-PT© with 34 subjects rated on a 5-point Likert scale questionnaire revealed high scores for all aspects of simulation, with 3.53 being the lowest mean score across all questions. A two-tailed Mann–Whitney test performed on the total scores showed significant (p = 0.001) difference between the groups. A similar test performed on the task time (p = 0.002) and the LOT (p = 0.004) separately showed statistically significant differences between the experts and the novices (p < 0.05). The experts appear to be traversing shorter overall trajectories in less time than the novices.


VBLaST-PT© showed both face and construct validity and has promise as a substitute for the FLS for training peg transfer skills.

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The authors gratefully acknowledge the support of this study by NIH/NIBIB (Grant No. 5R01EB010037). They also thank Alex Derevianko of Massachusetts General Hospital (MGH) for helping conduct the experiments and Saurabh Dargar of Rensselaer Polytechnic Institute for helping during the hardware design phase.


Venkata S. Arikatla, Dr. Ganesh Sankaranarayanan, Woojin Ahn, Amine Chellali, John Hwabejire, Marc DeMoya, Steven Schwaitzberg, Daniel B. Jones, Suvranu De, and Caroline Cao have no conflicts of interest or financial ties to disclose.

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Correspondence to Venkata S. Arikatla or Suvranu De.

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Arikatla, V.S., Sankaranarayanan, G., Ahn, W. et al. Face and construct validation of a virtual peg transfer simulator. Surg Endosc 27, 1721–1729 (2013).

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