Medical image interpretation training with a low‐cost eye tracking and feedback system: A preliminary study

Abstract

All medical students must learn to detect tumours in chest X-ray films. Eye tracking technology may be able to improve the training of this skill, with the potential benefit of saving thousands of lives each year. Eye trackers can record where students actually look in relation to known abnormalities in images, allowing the automated provision of corrective feedback during medical training. Research using dedicated eye tracking units suggests their potential as pedagogical tools but this hardware can be expensive, which prevents their use by medical students. To overcome this financial barrier, this project used eye tracking based on low-cost webcams to develop a system with the potential to allow all medical students to improve their interpretation skills. In self-study mode, students can practice and improve based on automated performance feedback in a browser-based system. Improvements in performance over time can be determined and mistakes can be played back and analysed for correction. Eight medical trainees used the system on a custom dataset of 60 chest X-ray images and were able to improve their decision times by self-study alone. The system was also rated highly for its ability to provide valuable objective information during subsequent one-on-one instruction sessions.