Scanpath comparison in medical image reading skills of dental students

Abstract

A popular topic in eye tracking is the difference between novices and experts and their domain-specific eye movement behaviors. However, very little is researched regarding how expertise develops, and more specifically, the developmental stages of eye movement behaviors. Our work compares the scanpaths of five semesters of dental students viewing orthopantomograms (OPTs) with classifiers to distinguish sixth semester through tenth semester students. We used the analysis algorithm SubsMatch 2.0 and the Needleman-Wunsch algorithm. Overall, both classifiers were able distinguish the stages of expertise in medical image reading above chance level. Specifically, it was able to accurately determine sixth semester students with no prior training as well as sixth semester students after training. Ultimately, using scanpath models to recognize gaze patterns characteristic of learning stages, we can provide more adaptive, gaze-based training for students.