Incorporation of Eye-Tracking and Gaze Feedback to Characterize and
Improve Radiologist Search Patterns of Chest X-rays: A Randomized Controlled
Clinical Trial
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.Comment: Submitted for Review in the Journal of the American College of
Radiology (JACR