AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study.

Karol Chlasta; Katarzyna Wisiecka; Krzysztof Krejtz; Izabela Krejtz

AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study.

Karol Chlasta, Katarzyna Wisiecka, Krzysztof Krejtz, Izabela Krejtz

Abstract

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, posing challenges in its quantification. Reduced well-being is often associated with depression or anxiety disorders, characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-aided screening of these affective disorders by analysing scan paths of visual attention using convolutional neural networks (CNNs). Data were collected during two studies assessing (1) attention tendencies among individuals diagnosed with major depression and (2) social anxiety. These data were applied to residual CNNs through images generated from eye-gaze patterns. The experimental results, obtained using ResNet architectures, demonstrated a promising 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 utilised in rapid, ecological, and effective mental health screening systems to quantify well-being through eye-tracking.

DOI: https://doi.org/10.54663/2182-9306.2024.SpecialIssueMBP.75-91

Keywords

Eye-tracking; Artificial intelligence; Convolutional neural networks; Depression; Social anxiety; Well-being

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