AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study
This is an incremental approach for mental health screening using eye-tracking, potentially aiding in rapid and ecological assessments of affective disorders.
The paper tackled AI-assisted screening for depression and social anxiety by analyzing eye-tracking data with convolutional neural networks, achieving an average accuracy of 48% for three-class and 62% for two-class classification.
Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduced well-being is often linked to depression or anxiety disorders, which are characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-assisted screening of affective disorders by analysing visual attention scan paths using convolutional neural networks (CNNs). Data were collected from two studies examining (1) attentional tendencies in individuals diagnosed with major depression and (2) social anxiety. These data were processed using residual CNNs through images generated from eye-gaze patterns. Experimental results, obtained with ResNet architectures, demonstrated an average accuracy of 48% for a three-class system and 62% for a two-class system. Based on these exploratory findings, we propose that this method could be employed in rapid, ecological, and effective mental health screening systems to assess well-being through eye-tracking.