Differentiating Surgeon Expertise Solely by Eye Movement Features
This work addresses the need for diagnostic and training models to optimize surgical training, though it is incremental as it builds on existing eye-tracking methods in medical contexts.
The study tackled the problem of differentiating surgeon expertise levels by analyzing eye movement features, achieving a classification accuracy of 76.46% for experts, 4th-year residents, and 3rd-year residents.
Developments in computer science in recent years are moving into hospitals. Surgeons are faced with ever new technical challenges. Visual perception plays a key role in most of these. Diagnostic and training models are needed to optimize the training of young surgeons. In this study, we present a model for classifying experts, 4th-year residents and 3rd-year residents, using only eye movements. We show a model that uses a minimal set of features and still achieve a robust accuracy of 76.46 % to classify eye movements into the correct class. Likewise, in this study, we address the evolutionary steps of visual perception between three expertise classes, forming a first step towards a diagnostic model for expertise.