The Learning Plateau and the Learning Rate for
the VBLaST PT© compared to the FLS simulator
Ganesh Sankaranarayanan PhD
April 24, 2013
Orlando/ASE 2013
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
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VBLaST System
VBLaST PC©
FLS and VBLaST PT© system
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.
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
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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
Simulator
Mean
Starting
Score
Learning
Plateau (a)
(Mean ± Std)
Learning
Rate
(10 * b/a)
(Mean ± Std)
VBLaST PT©
44.5 ± 10.51
94.03 ± 3.11
11 ± 3
FLS PT task
56.42 ± 15.11
94.97 ± 1.74
7±3
• Both simulators achieved a stabilized higher scores by the end of 150th trial
Learning in VBLaST
P < 0.00001 (pre and post test)
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
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Acknowledgments
 Funding from NIH, NIH/NIBIB 5R01EB010037
 Likun Zhang for conducting the study at the Tufts University School of
Medicine
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