LSTMs can distinguish dental expert saccade behavior with high "plaque-urracy"

Abstract

Much of the current expertise literature has found that domain specific tasks evoke different eye movements. However, research has yet to predict optimal image exploration using saccadic information and to identify and quantify differences in the search strategies between learners, intermediates, and expert practitioners. By employing LSTMs for scanpath classification, we found saccade features over time could distinguish all groups at high accuracy. The most distinguishing features were saccade velocity peak (72%), length (70%), and velocity average (68%). These findings promote the holistic theory of expert visual exploration that experts can quickly process the whole scene using longer and more rapid saccade behavior initially. The potential to integrate expertise model development from saccadic scanpath features into intelligent tutoring systems is the ultimate inspiration for our research. Additionally, this model is not confined to visual exploration in dental xrays, rather it can extend to other medical domains.