Mental Disease
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI model
An AI model provides an objective and rapid assessment of potential mental health risks in students by holistically analyzing their facial expressions, vocal characteristics, and linguistic content from data.
Sponsors
The Eye Hospital of Wenzhou Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
14 Years to 40 Years
Healthy volunteers
Yes
Inclusion criteria
* 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.
Exclusion criteria
* Inability to provide video or audio data of sufficient quality for analysis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | through study completion, an average of 1 year | — |
| AUROC | through study completion, an average of 1 year | Area Under the Receiver Operating Characteristic Curve |
| Specificity of the AI Model for Mental Health Screening | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Positive and Negative Predictive Values | through study completion, an average of 1 year | — |
| Correlation Between AI-Identified Risk Scores and Neurobiological Markers | through study completion, an average of 1 year | To assess the biological validity of the AI model, the model's output will be correlated with specific neurobiological markers obtained from a sub-study. The correlation will be assessed using a Pearson correlation coefficient. |
Countries
China
Outcome results
None listed