Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models
NCT ID: NCT06792175
Last Updated: 2025-09-03
Study Results
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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ENROLLING_BY_INVITATION
500 participants
OBSERVATIONAL
2025-02-04
2026-07-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Solicue (Any Mental Health Disorder)
Any participant enrolled in the study and not part of additional analysis group.
Solicue Machine Learning Models
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.
Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder)
Any participant enrolled in the study and exhibiting depressive symptoms as measured by PHQ-9 score.
Solicue Machine Learning Models
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 Models
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.
Interventions
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Solicue Machine Learning Models
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 Models
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.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
2. 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)
3. Fluent in English
4. Capable of providing informed consent, or in the case of minors, having a parent or legal guardian who can provide consent on their behalf.
5. Access to a device (smartphone, tablet, or computer) with a microphone and stable internet connectivity, necessary for completing the speech tasks.
Exclusion Criteria
2. Severe cognitive impairment or intellectual disability that would prevent understanding of the study procedures or completion of the speech tasks.
3. Lack of fluency in English.
4. Technical limitations: Inability to access a suitable device or internet connection for completing the speech tasks
13 Years
60 Years
ALL
No
Sponsors
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Allwell Behavioral Health Services
UNKNOWN
The Brookline Center
UNKNOWN
Psyrin Inc.
INDUSTRY
Responsible Party
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Principal Investigators
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Julianna Olah, B.Sc., M.A., M.Sc., Ph.D.
Role: PRINCIPAL_INVESTIGATOR
Psyrin Inc.
Atta-ul Raheem R Chaudhry, B.Sc. (Hons.), M.B.B.S.
Role: PRINCIPAL_INVESTIGATOR
Psyrin Inc.
Locations
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The Brookline Center
Brookline, Massachusetts, United States
Allwell Behavioral Health Services
Zanesville, Ohio, United States
Countries
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Other Identifiers
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PSYRIN-0004
Identifier Type: -
Identifier Source: org_study_id
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