Autism Spectrum DisorderMINDAIMNCT06792175
Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models
An observational study of Autism Spectrum Disorder and Depression - Major Depressive Disorder, run by Psyrin Inc. at 2 sites in United States.
Trial summary
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major… Read more about this study
- Who is it for?
- Ages 13 to 60 years
- Conditions the study is for
- Autism Spectrum Disorder, Depression - Major Depressive Disorder, Anxiety, Generalized
- What is being tested?
- Solicue Machine Learning Models and Mercuria Machine Learning Models
- How many people
- 500 participants
- Where
- 2 sitesUnited States
- Study dates
- 4 Feb 2025to Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 13 to 60 years
- All sexes
- Child, Adult
Study population: The MIND AIM study aims to recruit a diverse and representative sample of individuals seeking mental health assessments in various clinical settings. This broad inclusion criteria ensures high ecological validity, capturing the wide range of presentations and comorbidities commonly encountered in real-world mental health practice.
Who may be able to join
- Participants aged between 16 and 60 years.
- Individuals currently undergoing or referred for clinical assessment of mental or behavioral health conditions (including but not limited to ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, SSD)
- Fluent in English
- Capable of providing informed consent, or in the case of minors, having a parent or legal guardian who can provide consent on their behalf.
- Access to a device (smartphone, tablet, or computer) with a microphone and stable internet connectivity, necessary for completing the speech tasks.
Who cannot join
- Individuals experiencing acute mental health crises or severe symptoms that would preclude meaningful participation in the study, including acute intoxication.
- Severe cognitive impairment or intellectual disability that would prevent understanding of the study procedures or completion of the speech tasks.
- Lack of fluency in English.
- Technical limitations: Inability to access a suitable device or internet connection for completing the speech tasks
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites
The Brookline Center
Brookline, Massachusetts, 02445
Allwell Behavioral Health Services
Zanesville, Ohio, 43701
About this study
Official title: Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models: Testing Speech-Based Machine Learning Algorithms for Clinical Assessment and Risk Stratification in Mental Health Presentations
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.
- Conditions
- Autism Spectrum Disorder, Depression - Major Depressive Disorder, Anxiety, Generalized, Bipolar Disorder (BD), Attention Deficit Hyperactivity Disorder (ADHD), Schizophrenia Spectrum &Amp; Other Psychotic Disorders
- Being tested
- Solicue Machine Learning Models, Mercuria Machine Learning Models
- Keywords
- Artificial Intelligence, Natural Language Processing, Acoustic Analysis, Acoustic Biomarkers, Clinical Testing, Vocal Biomarkers, Machine Learning, Mental Health, Speech Analysis
- Medical subject headings
- Autism Spectrum Disorder, Generalized Anxiety Disorder, Bipolar Disorder, Attention Deficit Disorder with Hyperactivity, Stress Disorders, Post-Traumatic, Obsessive-Compulsive Disorder, Psychological Well-Being
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Solicue Machine Learning ModelsDiagnostic test
Also called Solicue, Psyrin Speech Analysis, Solicue Artificial Intelligence
A comprehensive machine-learning tool aimed at providing probability estimates for several compatible disorders, including Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Bipolar Affective Disorder (BPAD), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Obsessive Compulsive Disorder (OCD), Post-Traumatic Stress Disorder (PTSD), and Schizophrenia Spectrum Disorders (SSD). By offering a multi-diagnostic assessment based on speech analysis, Solicue aims to assist clinicians in navigating this complexity and potentially identifying conditions that might otherwise be overlooked in initial assessments.
Solicue leverages machine learning to analyze a wide range of clinically relevant speech features, including linguistic content, prosodic elements (such as pitch, rhythm, and intonation), and other paralinguistic features.
Mercuria Machine Learning ModelsDiagnostic test
Also called Mercuria, Mercuria Artificial Intelligence
Mercuria is designed to stratify the risk of bipolar disorder in individuals presenting with depressive symptoms. This is a critical clinical need, as misdiagnosis of bipolar disorder as unipolar depression is common and can lead to inappropriate treatment, potentially worsening outcomes. By analyzing speech patterns characteristic of bipolar disorder, Mercuria aims to provide an additional tool for clinicians to differentiate between these conditions more accurately, guiding appropriate treatment decisions.
Mercuria leverages machine learning to analyze a wide range of clinically relevant speech features, including linguistic content, prosodic elements (such as pitch, rhythm, and intonation), and other paralinguistic features.
| Study group | Role | What they receive |
|---|---|---|
| Solicue (Any Mental Health Disorder) | Any participant enrolled in the study and not part of additional analysis group. Receives: Diagnostic Test: Solicue Machine Learning Models | |
| Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder) | Any participant enrolled in the study and exhibiting depressive symptoms as measured by PHQ-9 score. Receives: Diagnostic Test: Solicue Machine Learning Models; Diagnostic Test: Mercuria Machine Learning Models |
What is being measured
The primary outcome is the main question the study is designed to answer.
Speech Battery ("PSY-10") audio
The speech battery consists of prompt-based tasks designed to elicit speech responses from participants in the form of monologues. This includes text reading, recall, and picture description tasks.
Measured: At initial assessment
Clinical diagnosis
Clinician diagnosis will be recorded for each participant at first assessment, 3-month, and 6-month follow-up. Diagnoses will be made according to ICD-11 or DSM-5 criteria for the compatible disorders: ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, and SSD. Additional relevant labels such as other mental health disorders, clinical high risk (CHR) and substance use may be recorded.
Measured: 0 months, 3 months, 6 months
Performance of AI models
The performance of the Mercuria and Solicue AI models will be evaluated using performance metrics of accuracy, balanced accuracy, sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, F1 score, AUC-ROC. Predicted labels will be compared with the ground truth clinical diagnoses obtained from the participating mental health clinics. Confidence acceptance threshold will be set.
Measured: 0 months, 3 months, 6 months
Secondary outcomes (4)
Patient Health Questionnaire-9 (PHQ-9)
The PHQ-9 is a 9-item self-reported questionnaire that assesses the severity of depressive symptoms.
Measured: At initial assessment
Mood Disorder Questionnaire (MDQ)
The MDQ is a 15-item self-report screening instrument designed to detect bipolar spectrum disorders. It consists of 13 yes/no questions about manic symptoms, followed by two questions about the co-occurrence and impact of these symptoms.
Measured: At initial assessment
DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC)
The DSM-5 Level 1 Cross-Cutting Symptom Measure is a 23-item self-report questionnaire that screens for 13 psychiatric domains, including depression, anxiety, and substance use.
Measured: At initial assessment
Reported Distress
To assess the safety of online speech assessment during clinical evaluation at initial intake. The safety of online speech assessment will be measured by severity of reported distress measured using the User Feedback Form (UFF).
Measured: After initial assessment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start4 Feb 2025
- Primary completionFeb 2026 (estimated)
- Study completionJul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Autism Spectrum DisorderStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Psyrin Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Allwell Behavioral Health Services, The Brookline Center
- Responsible party
- Sponsor
- Investigators
- Julianna Olah, B.Sc., M.A., M.Sc., Ph.D., Principal investigator, Psyrin Inc.Atta-ul Raheem R Chaudhry, B.Sc. (Hons.), M.B.B.S., Principal investigator, Psyrin Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06792175
- Sponsor's protocol ID
- PSYRIN-0004
- Acronym
- MINDAIM
- Status last verified
- Feb 2025
- First submitted
- 19 Jan 2025
- Last updated
- 3 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 3 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models: Testing Speech-Based Machine Learning Algorithms for Clinical Assessment and Risk Stratification in Mental Health Presentations
- ClinicalTrials.gov ID
- NCT06792175
- Acronym / sponsor ID
- MINDAIM / PSYRIN-0004
- Conditions
- Autism Spectrum Disorder, Depression - Major Depressive Disorder, Anxiety, Generalized, Bipolar Disorder (BD), Attention Deficit Hyperactivity Disorder (ADHD), Schizophrenia Spectrum &Amp; Other Psychotic Disorders
- Keywords
- Artificial Intelligence, Natural Language Processing, Acoustic Analysis, Acoustic Biomarkers, Clinical Testing, Vocal Biomarkers, Machine Learning, Mental Health
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Psyrin Inc.Industry (pharma, biotech, devices)
- Collaborators
- Allwell Behavioral Health Services, The Brookline Center
- Study start
- 4 Feb 2025 (actual)
- Primary completion
- Feb 2026 (estimated)
- Study completion
- Jul 2026 (estimated)
- First posted
- 24 Jan 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.
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Solicue (Any Mental Health Disorder) | – | Diagnostic Test: Solicue Machine Learning ModelsAny participant enrolled in the study and not part of additional analysis group. |
| Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder) | – | Diagnostic Test: Solicue Machine Learning Models, Diagnostic Test: Mercuria Machine Learning ModelsAny participant enrolled in the study and exhibiting depressive symptoms as measured by PHQ-9 score. |
Who may qualify
- Ages
- 13 to 60 years
- Sex
- All
- Age groups
- Child, Adult
- Study population
- The MIND AIM study aims to recruit a diverse and representative sample of individuals seeking mental health assessments in various clinical settings. This broad inclusion criteria ensures high ecological validity, capturing the wide range of presentations and comorbidities commonly encountered in real-world mental health practice.
Key inclusion criteria (5 listed)
- Participants aged between 16 and 60 years.
- Individuals currently undergoing or referred for clinical assessment of mental or behavioral health conditions (including but not limited to ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, SSD)
- Fluent in English
- Capable of providing informed consent, or in the case of minors, having a parent or legal guardian who can provide consent on their behalf.
- Access to a device (smartphone, tablet, or computer) with a microphone and stable internet connectivity, necessary for completing the speech tasks.
Key exclusion criteria (4 listed)
- Individuals experiencing acute mental health crises or severe symptoms that would preclude meaningful participation in the study, including acute intoxication.
- Severe cognitive impairment or intellectual disability that would prevent understanding of the study procedures or completion of the speech tasks.
- Lack of fluency in English.
- Technical limitations: Inability to access a suitable device or internet connection for completing the speech tasks
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 |
|---|---|---|
| Solicue Machine Learning ModelsAlso: Solicue, Psyrin Speech Analysis, Solicue Artificial IntelligenceA comprehensive machine-learning tool aimed at providing probability estimates for several compatible disorders, including Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Bipolar Affective Disorder (BPAD), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Obsessive Compulsive Disorder (OCD), Post-Traumatic Stress Disorder (PTSD), and Schizophrenia Spectrum Disorders (SSD). By offering a multi-diagnostic assessment based on speech analysis, Solicue aims to assist clinicians in navigating this complexity and potentially identifying conditions that might otherwise be overlooked in initial assessments. Solicue leverages machine learning to analyze a wide range of clinically relevant speech features, including linguistic content, prosodic elements (such as pitch, rhythm, and intonation), and other paralinguistic features. | Diagnostic test | Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder), Solicue (Any Mental Health Disorder) |
| Mercuria Machine Learning ModelsAlso: Mercuria, Mercuria Artificial IntelligenceMercuria is designed to stratify the risk of bipolar disorder in individuals presenting with depressive symptoms. This is a critical clinical need, as misdiagnosis of bipolar disorder as unipolar depression is common and can lead to inappropriate treatment, potentially worsening outcomes. By analyzing speech patterns characteristic of bipolar disorder, Mercuria aims to provide an additional tool for clinicians to differentiate between these conditions more accurately, guiding appropriate treatment decisions. Mercuria leverages machine learning to analyze a wide range of clinically relevant speech features, including linguistic content, prosodic elements (such as pitch, rhythm, and intonation), and other paralinguistic features. | Diagnostic test | Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder) |
Primary outcome
Speech Battery ("PSY-10") audio
Time frame: At initial assessment
The speech battery consists of prompt-based tasks designed to elicit speech responses from participants in the form of monologues. This includes text reading, recall, and picture description tasks.
Clinical diagnosis
Time frame: 0 months, 3 months, 6 months
Clinician diagnosis will be recorded for each participant at first assessment, 3-month, and 6-month follow-up. Diagnoses will be made according to ICD-11 or DSM-5 criteria for the compatible disorders: ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, and SSD. Additional relevant labels such as other mental health disorders, clinical high risk (CHR) and substance use may be recorded.
Performance of AI models
Time frame: 0 months, 3 months, 6 months
The performance of the Mercuria and Solicue AI models will be evaluated using performance metrics of accuracy, balanced accuracy, sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, F1 score, AUC-ROC. Predicted labels will be compared with the ground truth clinical diagnoses obtained from the participating mental health clinics. Confidence acceptance threshold will be set.
4 secondary outcomes
Patient Health Questionnaire-9 (PHQ-9)
Time frame: At initial assessment
Mood Disorder Questionnaire (MDQ)
Time frame: At initial assessment
DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC)
Time frame: At initial assessment
Reported Distress
Time frame: After initial assessment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Sep 2025.
- Study websiteLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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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.
Julianna Olah, B.Sc., M.A., M.Sc., Ph.D. principal investigator
Psyrin Inc.
Atta-ul Raheem R Chaudhry, B.Sc. (Hons.), M.B.B.S. principal investigator
Psyrin Inc.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Autism Spectrum Disorder and Depression - Major Depressive Disorder, run by Psyrin Inc. at 2 sites in United States.
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 5 inclusion and 4 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: enrolling by invitation, as of the sponsor's last update on 3 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. 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?
This study is still under way. Results are not available in the source record yet.
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?
Speech Battery ("PSY-10") audio (time frame: At initial assessment). 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 3 Sep 2025; AzTrials last refreshed its copy on 6 Nov 2026.