Incorporation of Eye-Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-rays: A Randomized Controlled Clinical Trial

TL;DR

This study demonstrates that automated feedback using eye-tracking technology significantly improves radiologists' accuracy in detecting pulmonary nodules in chest X-rays, highlighting a promising approach for training enhancement.

Contribution

The paper introduces an automated, feedback-driven educational framework utilizing eye-tracking to improve radiologist search patterns and diagnostic accuracy.

Findings

Intervention group improved detection accuracy by 38.89%.

Control group showed only 5.56% improvement.

Rapid improvement observed over four training sessions.

Abstract

Diagnostic errors in radiology often occur due to incomplete visual assessments by radiologists, despite their knowledge of predicting disease classes. This insufficiency is possibly linked to the absence of required training in search patterns. Additionally, radiologists lack consistent feedback on their visual search patterns, relying on ad-hoc strategies and peer input to minimize errors and enhance efficiency, leading to suboptimal patterns and potential false negatives. This study aimed to use eye-tracking technology to analyze radiologist search patterns, quantify performance using established metrics, and assess the impact of an automated feedback-driven educational framework on detection accuracy. Ten residents participated in a controlled trial focused on detecting suspicious pulmonary nodules. They were divided into an intervention group (received automated feedback) and a control group. Results showed that the intervention group exhibited a 38.89% absolute improvement in detecting suspicious-for-cancer nodules, surpassing the control group's improvement (5.56%, p-value=0.006). Improvement was more rapid over the four training sessions (p-value=0.0001). However, other metrics such as speed, search pattern heterogeneity, distractions, and coverage did not show significant changes. In conclusion, implementing an automated feedback-driven educational framework improved radiologist accuracy in detecting suspicious nodules. The study underscores the potential of such systems in enhancing diagnostic performance and reducing errors. Further research and broader implementation are needed to consolidate these promising results and develop effective training strategies for radiologists, ultimately benefiting patient outcomes.