Comparing Visual Search Patterns in Chest X-Ray Diagnostics

Radiologists are trained professionals who use medical images to obtain clinically relevant information. However, little is known during medical image analysis. Thus, there is a current need for guidelines to specify optimal visual search routines commonly used by radiologists. Identifying these features could improve radiologist training and assist radiologists in their work. Our study found that during the moments in which radiologists view chest X-ray images in silence before verbalizing the analysis, they exhibit unique search patterns regardless of the type of disease depicted. Our findings suggest that radiologists' search behaviors can be identified at this stage. However, when radiologists verbally interpret the X-rays, the gaze patterns appear noisy and arbitrary. Current deep-learning approaches train their systems using this noisy and arbitrary gaze data. This may explain why previous research still needs to show the superiority of deep-learning models that use eye tracking for disease classification. Our paper investigates these patterns and attempts to uncover the eye-gaze configurations during the different analysis phases.