  Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental
disorder that is highly prevalent and requires clinical specialists to
diagnose. It is known that an individual's viewing behavior, reflected in their
eye movements, is directly related to attentional mechanisms and higher-order
cognitive processes. We therefore explore whether ADHD can be detected based on
recorded eye movements together with information about the video stimulus in a
free-viewing task. To this end, we develop an end-to-end deep learning-based
sequence model which we pre-train on a related task for which more data are
available. We find that the method is in fact able to detect ADHD and
outperforms relevant baselines. We investigate the relevance of the input
features in an ablation study. Interestingly, we find that the model's
performance is closely related to the content of the video, which provides
insights for future experimental designs.
