States of confusion: Eye and Head tracking reveal surgeons' confusion during arthroscopic surgery

TL;DR

This paper presents a machine learning approach to detect surgeons' confusion during arthroscopic surgery by analyzing eye and head movements, achieving over 94% accuracy and enabling real-time diagnostic support.

Contribution

The study introduces a novel method for recognizing surgeons' confusion states in real-time using eye and head tracking data with high accuracy.

Findings

Achieved over 94% accuracy in detecting confusion.

Detection speed of 0.039 seconds enables real-time application.

Potential for online diagnostic and training systems.

Abstract

During arthroscopic surgeries, surgeons are faced with challenges like cognitive re-projection of the 2D screen output into the 3D operating site or navigation through highly similar tissue. Training of these cognitive processes takes much time and effort for young surgeons, but is necessary and crucial for their education. In this study we want to show how to recognize states of confusion of young surgeons during an arthroscopic surgery, by looking at their eye and head movements and feeding them to a machine learning model. With an accuracy of over 94\% and detection speed of 0.039 seconds, our model is a step towards online diagnostic and training systems for the perceptual-cognitive processes of surgeons during arthroscopic surgeries.