An Artificial Intelligence-Based Screening Tool to Detect Psychological Distress
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 17,386
- 试验地点
- 1
- 主要终点
- Sensitivity
研究概览
简要总结
The goal of this observational study is to test an artificial intelligence (AI) tool that can help screen for mental health risks . The main questions it aims to answer are:
Can an AI model that analyzes a person's voice, facial expressions, and language accurately identify students who may be at high risk for mental health conditions, such as depression or OCD?
How accurate is the AI model when compared to results from standard mental health questionnaires?
Participants will be asked to:
Complete a standard mental health questionnaire.
Provide consent for their data to be used in the research.
Participate in a recorded session to collect video and audio data for the AI model to analyze.
详细描述
This large-scale, multi-center observational study aims to develop and validate a novel artificial intelligence (AI) model for the early and objective screening of mental health risks, such as depression and OCD, in university students. The model will be trained and internally validated on multimodal data (including vocal, facial, and linguistic features) from a large student cohort. A subsequent neuroscience sub-study will explore the neurobiological correlates of the AI-identified risk levels using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to establish biological validity. The primary outcome is to assess the final model's diagnostic accuracy, quantified by its sensitivity, specificity, and AUC, with the ultimate goal of providing a scalable and efficient early warning tool to facilitate timely clinical intervention for university populations.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 14 Years 至 40 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Enrolled as a student at a participating university.
- •Age between 14 and 40 years, inclusive.
- •Willing and able to provide written informed consent.
- •Fluent in the language required for the study.
排除标准
- •Inability to provide video or audio data of sufficient quality for analysis.
结局指标
主要结局
Sensitivity
时间窗: through study completion, an average of 1 year
AUROC
时间窗: through study completion, an average of 1 year
Area Under the Receiver Operating Characteristic Curve
Specificity of the AI Model for Mental Health Screening
时间窗: through study completion, an average of 1 year
The ability of the AI model to correctly identify students without significant psychological distress. It will be calculated as the percentage of participants correctly classified as 'low-risk' by the AI model compared to a 'gold standard' classification
次要结局
- Correlation Between AI-Identified Risk Scores and Neurobiological Markers(through study completion, an average of 1 year)
- Positive and Negative Predictive Values(through study completion, an average of 1 year)
研究者
Kang Zhang
Professor
The Eye Hospital of Wenzhou Medical University