Incorporation of Eye-Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-rays: A Randomized Controlled Clinical Trial
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…
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Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
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