Derek Young, Fiona Slevin, Derek Cassidy, Donncha Ryan, Haptica Ltd, Dublin, Ireland.

Purpose

The ProMIS surgical simulator has been well-validated for measuring Fundamentals of Laparoscopic Surgery (FLS) Tasks1 2 3 4

The Precision-Cutting Task in FLS requires the user to dissect a circle of specific size and shape from a marked piece of mesh. Currently, measurement of the accuracy and area dissected is done by observation and by measuring the dissected mesh on a measurement grid.

Using advanced image processing, ProMIS takes an image of the dissected mesh and automatically generates a metric on the actual dissected area versus the target area.

Method

1.  The FLS Precision-Cutting Task is placed in ProMIS and the user completes it according to FLS instructions.

2. Once the user has completed the Task, ProMIS takes an image of the dissected mesh. The image is converted to binary image and then scanned using a pattern recognition algorithm which can identify the ‘cut out’ area for isolation and analysis.

3.  The actual measurement for the dissected area is given in cm2. This is calculated by counting the number of pixels in the known area of the image and then using ratios to determine the area of the ‘cut out’.   

 Valid performance metrics

ProMIS measures surgical proficiency by tracking instruments and objects (including hand-movement) in a standardized task.

Standard metrics for all Tasks include time taken, total path swept by each instrument tip, and economy of movement. These metrics have been shown to be valid by over 25 studies (see www.haptica.com).

Task-specific metrics are also provided. For example, in a Peg-Transfer task, ProMIS can detect the path of the pegs – thus detecting errors such as dropped pegs.

In Precision-Cutting, the ProMIS tracking system uses an image of the completed task to deliver a precise calculation of the area dissected versus the optimal area.

A full analysis of performance is presented to the user on completion of each Task.

Results

Initial trials of the metrics on the FLS Precision-Cutting Task show that ProMIS is more accurate than the current human observation method which relies on the collection of the area into squares.

Conclusions

Mirroring the view of Ritter et al 20061, the ability of ProMIS to measure the FLS Precision-Cutting Task accurately could accelerate the pace of FLS certification by augmenting labor intensive human observation with ProMIS metrics.

References

1 SAGES 2006 S064 Ritter et al, Concurrent Validity of Augmented Reality Metrics applied to the Fundamentals of Laparoscopic Surgery (FLS)

2 SAGES 2006 P237 McCluney MD, et al, Validation of the ProMIS Hybrid Simulator using a Standard Set of Laparoscopic Tasks

3 SAGES 2007 P279 McCluney et al, Automated ProMIS Simulator Metrics Predict Readiness for FLS Certification

4 SAGES 2006 S065 Vuong et al, What Can Motion Derivatives Tell Us About Skill Performance?

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