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Automatic scoring of virtual mastoidectomies using expert examples.

Thomas Kerwin¹, Gregory Wiet, Don Stredney

¹Ohio Supercomputer Center, Columbus, OH, USA. kerwin@osc.edu

International Journal of Computer Assisted Radiology and Surgery

|May 4, 2011

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This study introduces an automated system for scoring resident performance in virtual mastoidectomy simulations. The novel approach offers objective, efficient evaluations and improved reliability metrics compared to previous methods.

Area of Science:

Medical Simulation
Surgical Education Technology

Background:

Consistent and efficient evaluation of surgical skills is crucial for resident training.
Virtual reality simulation systems offer a controlled environment for practicing complex procedures like mastoidectomy.

Purpose of the Study:

To develop and validate an automatic scoring system for resident performance on a virtual mastoidectomy simulation.
To provide objective and efficient performance assessments without requiring immediate expert intervention.

Main Methods:

A segmented dataset of a virtual temporal bone was created, defining surgically important regions.
Performance was evaluated by comparing resident-drilled virtual bones to expert-drilled examples using Euclidean and Earth Mover's Distance.
A decision tree algorithm was developed to score surgical performance based on these comparisons, with reliability assessed using averaged scores from multiple expert examples.

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Main Results:

The developed multi-grade scoring system demonstrated improved reliability metrics compared to existing binary classification methods.
Two distinct scoring methods were implemented, offering a trade-off between computational speed and accuracy.

Conclusions:

Voxel-level comparison of virtually drilled bones against expert examples provides sufficient data for performance scoring and quality metrics.
Two scoring metrics were developed by merging data from multiple expert examples, catering to different needs for speed versus accuracy.