Mental DiseaseNCT07092085
AI-Powered Mental Health Screening in University Students
An observational study of Mental Disease, run by The Eye Hospital of Wenzhou Medical University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 14 to 40 yearsHealthy volunteers can take part
- Conditions the study is for
- Mental Disease
- What is being tested?
- AI model
- How many people
- 17,386 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2023to 1 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 14 to 40 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
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.
Who may be able to join
- 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.
Who cannot join
- Inability to provide video or audio data of sufficient quality for analysis.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: An Artificial Intelligence-Based Screening Tool to Detect Psychological Distress
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.
Read the 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.
- Conditions
- Mental Disease
- Being tested
- AI model
- Medical subject headings
- Mental Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI modelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Healthy Control | without mental healthy problem Receives: Diagnostic Test: AI model | |
| Mental Diseases | with mental problem, such as depression, OCD Receives: Diagnostic Test: AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Measured: through study completion, an average of 1 year
AUROC
Area Under the Receiver Operating Characteristic Curve
Measured: 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
Measured: through study completion, an average of 1 year
Secondary outcomes (2)
Positive and Negative Predictive Values
Measured: through study completion, an average of 1 year
Correlation Between AI-Identified Risk Scores and Neurobiological Markers
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.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2025
- Study start1 Mar 2023
- Primary completion1 Feb 2025
- Study completion1 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mental DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 17,386 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, The Eye Hospital of Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07092085
- Sponsor's protocol ID
- Mental Health
- Status last verified
- Jul 2025
- First submitted
- 10 Jul 2025
- Last updated
- 29 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence-Based Screening Tool to Detect Psychological Distress
- ClinicalTrials.gov ID
- NCT07092085
- Acronym / sponsor ID
- Mental Health
- Conditions
- Mental Disease
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 17,386 participants (actual)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 1 Feb 2025 (actual)
- Study completion
- 1 Apr 2025 (actual)
- First posted
- 29 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Healthy Control | – | Diagnostic Test: AI modelwithout mental healthy problem |
| Mental Diseases | – | Diagnostic Test: AI modelwith mental problem, such as depression, OCD |
Who may qualify
- Ages
- 14 to 40 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (1 listed)
- Inability to provide video or audio data of sufficient quality for analysis.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI modelAn 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. | Diagnostic test | Healthy Control, Mental Diseases |
Primary outcome
Sensitivity
Time frame: through study completion, an average of 1 year
AUROC
Time frame: through study completion, an average of 1 year
Area Under the Receiver Operating Characteristic Curve
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
2 secondary outcomes
Positive and Negative Predictive Values
Time frame: through study completion, an average of 1 year
Correlation Between AI-Identified Risk Scores and Neurobiological Markers
Time frame: through study completion, an average of 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mental Disease, run by The Eye Hospital of Wenzhou Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: through study completion, an average of 1 year). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jul 2025.