Differentiating Surgeon Expertise Solely by Eye Movement Features

TL;DR

This paper introduces a model that classifies surgeon expertise levels based solely on eye movement features, achieving over 76% accuracy, which could aid in training and diagnostics.

Contribution

It presents a novel approach using minimal eye movement features to differentiate surgeon expertise levels, advancing diagnostic and training tools in medical education.

Findings

Achieved 76.46% classification accuracy.

Identified evolutionary differences in visual perception among expertise levels.

Established a foundation for diagnostic models based on eye movements.

Abstract

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.