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.

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.

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.

The study population comprises a large cohort of students recruited from multiple universities. Participants will be included in the main phase for AI model development. Additionally, a sub-group of students will be selected based on their mental health risk levels (e.g., for depression or OCD) to participate in a subsequent neuroscience sub-study.