Detection of ADHD based on Eye Movements during Natural Viewing
This addresses ADHD diagnosis, which is prevalent and requires specialists, by offering a potential automated tool based on eye-tracking, though it appears incremental as it builds on existing methods.
The paper tackled detecting ADHD using eye movements during natural viewing, developing a deep learning sequence model that outperformed baselines and found performance depended on video content.
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.