Collaborative Agents for Anxious Depression Diagnosis and Intervention Leverage Neuroimages in Human Brain-AI Interactions

Depressive disorders to some extent erode the mental and physical health of many individuals. There is a pressing need for new technologies to enhance the diagnosis as well as treatment of depression, which currently relies heavily on subjective reports and lacks precise classification for its subtypes. Anxiety-type depression is a subtype of depression that is clinically challenging to distinguish from non-anxiety-type depression. Statistical data indicates anxiety-type depression has a higher prevalence among university students, poses greater risks, and has more possibility of leading to suicidal tendencies. Research integrating neuroimaging with artificial intelligence for assisted diagnosis and treatment of anxiety-type depression holds significant clinical importance. This study collected fNIRS data from depression patients and evaluated classification performance using a deep learning measure based on the CNN framework, highlighting the extraction of the most significant feature associated with anxiety-type depression deficits. This work provides a valuable lens to understand the complexities of anxiety-type depression and design human-computer interaction treatment methods. Drawing on research in psychology and cognitive science, and recent advancements in cognitive intervention studies, we illustrate the demands of assisted diagnosis, requiring a high degree of intelligent treatment of depression. We propose addressing these demands by designing and integrating a social-assistive chatbot that employs CBT for treating anxiety-type depression into a comprehensive agent system.