Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models

NCT06792175 | Enrolling By Invitation FDA Device

• 1 other identifier: PSYRIN-0004
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 2

Brief 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 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; Post Traumatic Stress Disorder; Obsessive Compulsive Disorder (OCD)

Keywords

Artificial Intelligence; Natural Language Processing; Acoustic Analysis; Acoustic Biomarkers; Clinical Testing; Vocal Biomarkers; Machine Learning; Mental Health; Speech Analysis

Outcome Measures

Primary Outcomes (3)

• 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.

  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.

  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.

  0 months, 3 months, 6 months

Secondary Outcomes (4)

• Patient Health Questionnaire-9 (PHQ-9)

  At initial assessment

• Mood Disorder Questionnaire (MDQ)

  At initial assessment

• DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC)

  At initial assessment

• Reported Distress

  After initial assessment

Study Arms (2)

Solicue (Any Mental Health Disorder)

Any participant enrolled in the study and not part of additional analysis group.

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.

Diagnostic Test: Solicue Machine Learning Models; Diagnostic Test: Mercuria Machine Learning Models

Interventions

Solicue Machine Learning Models DIAGNOSTIC_TEST

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.

Also known as: Solicue, Psyrin Speech Analysis, Solicue Artificial Intelligence

Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder); Solicue (Any Mental Health Disorder)

Mercuria Machine Learning Models DIAGNOSTIC_TEST

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.

Also known as: Mercuria, Mercuria Artificial Intelligence

Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder)

Eligibility Criteria

• Age: 13 Years - 60 Years
• Sex: all
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Psyrin Inc.: lead
• Allwell Behavioral Health Services: collaborator
• The Brookline Center: collaborator

Study Sites (2)

The Brookline Center

Brookline, Massachusetts, 02445, United States

Location

Allwell Behavioral Health Services

Zanesville, Ohio, 43701, United States

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Audio of speech collected

MeSH Terms

Conditions

Autism Spectrum Disorder; Generalized Anxiety Disorder; Bipolar Disorder; Attention Deficit Disorder with Hyperactivity; Stress Disorders, Post-Traumatic; Obsessive-Compulsive Disorder; Psychological Well-Being

Condition Hierarchy (Ancestors)

Child Development Disorders, Pervasive; Neurodevelopmental Disorders; Mental Disorders; Anxiety Disorders; Bipolar and Related Disorders; Mood Disorders; Attention Deficit and Disruptive Behavior Disorders; Stress Disorders, Traumatic; Trauma and Stressor Related Disorders; Personal Satisfaction; Behavior

Study Officials

• Julianna Olah, B.Sc., M.A., M.Sc., Ph.D.

  Psyrin Inc.

  PRINCIPAL INVESTIGATOR

• Atta-ul Raheem R Chaudhry, B.Sc. (Hons.), M.B.B.S.

  Psyrin Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: February 4, 2025
• Primary Completion: February 1, 2026
• Study Completion (Estimated): July 1, 2026
• Last Updated: September 3, 2025

Record last verified: 2025-02

Locations

US (2)