AI-Powered Mental Health Screening in University Students

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

17386 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-03-01

Study Completion Date

2025-04-01

Brief Summary

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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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Detailed Description

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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.

Conditions

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Mental Disease

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Study Groups

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Healthy Control

without mental healthy problem

AI model

Intervention Type 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.

Mental Diseases

with mental problem, such as depression, OCD

AI model

Intervention Type 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.

Interventions

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AI 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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.