Ganesh Sankaranarayanan PhDApril 24, 2013

Orlando/ASE 2013

The Learning Plateau and the Learning Rate for the VBLaST PT© compared to the FLS simulator

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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.

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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

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)

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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