PsyDraw：一种用于留守儿童心理健康筛查的多智能体多模态系统
PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
AI总结：
提出基于多模态大语言模型的多智能体系统PsyDraw，协助专业人员分析房树人绘画以筛查留守儿童心理健康，在290名学生测试中展现出高专业一致性和有效性。
摘要：
中国留守儿童（LBCs）数量超过6600万，因父母外出务工面临严重的心理健康挑战。对处于风险的LBCs进行早期筛查和识别至关重要，但由于心理健康专业人员严重短缺（尤其是在农村地区），这充满挑战。尽管房树人（HTP）测试显示出较高的儿童参与率，但其对专家解读的要求限制了其在资源匮乏地区的应用。为应对这一挑战，我们提出了PsyDraw，一个基于多模态大语言模型的多智能体系统，协助心理健康专业人员分析HTP绘画。该系统采用专门的智能体进行特征提取和心理解读，分两个阶段运行：综合特征分析和专业报告生成。对290名小学生的HTP绘画评估显示，71.03%的分析与专业评估达到高度一致，26.21%达到中度一致，仅2.41%为低度一致。该系统识别出31.03%的案例需要专业关注，证明其作为初步筛查工具的有效性。目前已在试点学校部署，PsyDraw在支持心理健康专业人员方面展现出前景，特别是在资源有限的地区，同时在心理评估中保持高专业标准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
