Ganesh Sankaranarayanan PhD April 24, 2013 Orlando/ASE 2013 The Learning Plateau and the Learning Rate for the VBLaST PT© compared to the FLS simulator

cemsim.rpi.edu Introduction -The Virtual Basic Laparoscopic Skills Trainer (VBLaST © ) is a virtual reality simulator that is capable of simulating the Fundamentals of Laparoscopic Surgery (FLS) tasks. -Has a custom interface with haptic (force) feedback capabilities. -Can compute scores automatically -No need for proctors -No need to replenish materials -Additional performance measures can be measured/coded any time

VBLaST System FLS and VBLaST PT © system VBLaST PC © VBLaST LP ©

VBLaST PT ©  Can simulate the peg transfer task  The simulator has shown -Concurrent validity -Convergent validity

Learning Curve Study ( Convergent Validity)  Three groups -Control (no training) -VBLaST -FLS  15 sessions (10 trials each session) -5 days x 3 weeks -Pre-test, post-test, retention test (2 weeks after post test)  Normalized numerical score based on completion time and errors were calculated for both the systems  18 medical students from the Tufts University School of Medicine were recruited in this IRB approved study.  Cumulative Summation Method (CUSUM) was used for assessing the learning curve of both VBLaST and the FLS systems.

cemsim.rpi.edu Need for Learning Plateau and the Learning Rate  CUMSUM method is criterion based -Junior, intermediate, senior -MISTELS (Fraser et al.) -VBLaST (Zhang et al.) -Can track performance with every single trial  Learning curve has three distinct parameters (Cook et al.) -Starting point ( where the performance starts) -The plateau ( where the performance flattens) -Learning rate ( how fast the performance level is reached)  The parameters are intuitive and easy to relate scores to performance

Inverse Curve Fitting  Inverse curve Y = a – b/X  a is the theoretical maximum score  b is the slope  b/a is the rate  10 * b/a was defined as the number of trials to reach 90% of the asymptote  First defined and used for learning curve by Feldman et al.  Parameters computed using nonlinear regression  IBM PASW 18 was used for analysis

Results - Curve Fitting VBLaST PT © FLS

Results – Learning Curve Parameters SimulatorMean Starting Score Learning Plateau (a) (Mean ± Std) Learning Rate (10 * b/a) (Mean ± Std) VBLaST PT©44.5 ± ± ± 3 FLS PT task56.42 ± ± ± 3 Both simulators achieved a stabilized higher scores by the end of 150 th trial

Learning in VBLaST P < (pre and post test)

cemsim.rpi.edu Discussion  Inverse curve fitting showed stable plateaus for both the simulators  Learning rate was lower in VBLaST compared to FLS -Similarly the CUSUM analysis also showed more number of trials to achieve the Junior, Intermediate and senior levels  VBLaST is a virtual reality simulator -Still requires some adaptation by users, especially when used for first time -Other solutions that are being currently implemented in the second generation of the VBLaST simulators are -Workspace matching -Tool peg interactions ( picking and transfer) as realistic to the FLS

cemsim.rpi.edu Acknowledgments  Funding from NIH, NIH/NIBIB 5R01EB  Likun Zhang for conducting the study at the Tufts University School of Medicine