PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
Left-behind children (LBCs), numbering over 66 million in China, face severe
mental health challenges due to parental migration for work. Early screening
and identification of at-risk LBCs is crucial, yet challenging due to the
severe shortage of mental health professionals, especially in rural areas.
While the House-Tree-Person (HTP) test shows higher child participation rates,
its requirement for expert interpretation limits its application in
resource-scarce regions. To address this challenge, we propose PsyDraw, a
multi-agent system based on Multimodal Large Language Models that assists
mental health professionals in analyzing HTP drawings. The system employs
specialized agents for feature extraction and psychological interpretation,
operating in two stages: comprehensive feature analysis and professional report
generation. Evaluation of HTP drawings from 290 primary school students reveals
that 71.03% of the analyzes achieved High Consistency with professional
evaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The
system identified 31.03% of cases requiring professional attention,
demonstrating its effectiveness as a preliminary screening tool. Currently
deployed in pilot schools, \method shows promise in supporting mental health
professionals, particularly in resource-limited areas, while maintaining high
professional standards in psychological assessment.
