AI-Powered Mental Health Screening in University Students

NCT07092085 | Completed DMC

• 1 other identifier: Mental Health
• Study Type: observational
• Target: 17,386
• Locations: 1 country
• Sites: 1

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.

Conditions

Mental Disease

Outcome Measures

Primary Outcomes (3)

• Sensitivity

  through study completion, an average of 1 year

• AUROC

  Area Under the Receiver Operating Characteristic Curve

  through study completion, an average of 1 year

• Specificity of the AI Model for Mental Health Screening

  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

  through study completion, an average of 1 year

Secondary Outcomes (2)

• 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

Study Arms (2)

Healthy Control

without mental healthy problem

Diagnostic Test: AI model

Mental Diseases

with mental problem, such as depression, OCD

Diagnostic Test: AI model

Interventions

AI model DIAGNOSTIC_TEST

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.

Healthy Control; Mental Diseases

Eligibility Criteria

• Age: 14 Years - 40 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• Inability to provide video or audio data of sufficient quality for analysis.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (1)

Peking Union Medical College

Beijing, Beijing Municipality, China

Location

MeSH Terms

Conditions

Mental Disorders

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: July 10, 2025
• First Posted: July 29, 2025
• Study Start: March 1, 2023
• Primary Completion: February 1, 2025
• Study Completion: April 1, 2025
• Last Updated: July 29, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)