AI-Powered Mental Health Screening in University Students

Completed

• Conditions: Mental Disease

• Registration Number: NCT07092085
• Lead Sponsor: The Eye Hospital of Wenzhou Medical University

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.

Study Timeline

• Study Start: 2023-03-01
• Primary Completion: 2025-02-01
• Study Completion: 2025-04-01
• Status Verified: 2025-07
• Study First Submitted: 2025-07-10
• Study First Submitted QC: 2025-07-21
• Last Update Submitted: 2025-07-21
• Study First Posted: 2025-07-29
• Last Update Posted: 2025-07-29

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 17386

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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.
Positive and Negative Predictive Values	through study completion, an average of 1 year

Trial Locations

Locations (1)

Peking Union Medical College; 🇨🇳 Beijing, Beijing, China