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OpenTrials
Completed

NCT Number: NCT07092085

AI-Powered Mental Health Screening in University Students

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.

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Key information

Conditions

Mental Disease Mental Disorders

Age range

14 year–40 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking Union Medical College

Beijing, Beijing Municipality, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

  1. Sensitivity

    Time frame: through study completion, an average of 1 year

  2. AUROC

    Time frame: through study completion, an average of 1 year

    Area Under the Receiver Operating Characteristic Curve

  3. Specificity of the AI Model for Mental Health Screening

    Time frame: 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 outcomes

  1. Positive and Negative Predictive Values

    Time frame: through study completion, an average of 1 year

  2. Correlation Between AI-Identified Risk Scores and Neurobiological Markers

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

The Eye Hospital of Wenzhou Medical University

Other

Registry information

Official study title

An Artificial Intelligence-Based Screening Tool to Detect Psychological Distress

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Jul 29, 2025
Registry last updated
Jul 29, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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