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.

The association of attentional foci and image interpretation accuracy in novices interpreting lung ultrasound images: an eye-tracking study

“…From a training perspective, eye-tracking data may provide feedback to learners [ 22 ], by demonstrating where errors in attention may lie. Prior studies on non-ultrasound imaging suggest that training using eye movement feedback data may increase interpretation accuracy [ 23 , 24 ] and improve decision time [ 23 , 25 ]. One randomized study training learners where to look in ultrasound videos using eye movement technology found higher interpretation accuracy [ 26 ].…”

Online eye tracking and real-time sentence processing: On opportunities and efficacy for capturing psycholinguistic effects of different magnitudes and diversity

“…Further work suggests that webcam-based eye tracking in combination with WebGazer.js is a promising new tool in areas such as online video education (Madsen et al, 2021 ), mental state assessment (Paletta et al, 2020 ; Greenaway et al, 2021 ), medical image interpretation training (Quen et al, 2021 ), and episodic memory (Calabrich et al, 2021 ). All studies point out that the gaze position data collected with webcams (as opposed to specialized hardware) is noisier; however, they also point out that given how promising it is, this technology will be enhanced going forward and thus has huge potential.…”