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Enrolling by Invitation

NCT Number: NCT06792175

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

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.

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Key information

Conditions

Autism Spectrum Disorder Anxiety Disorders Anxiety, Generalized Attention Deficit Disorder with Hyperactivity Attention Deficit Hyperactivity Disorder (ADHD) Attention Deficit and Disruptive Behavior Disorders Behavior Bipolar Disorder Bipolar Disorder (BD) Bipolar and Related Disorders Child Development Disorders, Pervasive Depression - Major Depressive Disorder Generalized Anxiety Disorder Mental Disorders Mood Disorders Neurodevelopmental Disorders Obsessive Compulsive Disorder (OCD) Obsessive-Compulsive Disorder Personal Satisfaction Post Traumatic Stress Disorder Psychological Well-Being Schizophrenia Spectrum &Amp; Other Psychotic Disorders Stress Disorders, Post-Traumatic Stress Disorders, Traumatic Trauma and Stressor Related Disorders

Age range

13 year–60 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The Brookline Center, Brookline, Massachusetts, United States

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Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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

Treatment and study plan

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.

Other names: Solicue, Psyrin Speech Analysis, Solicue Artificial Intelligence

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.

Other names: Mercuria, Mercuria Artificial Intelligence

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

Secondary outcomes

  1. Patient Health Questionnaire-9 (PHQ-9)

    Time frame: At initial assessment

    The PHQ-9 is a 9-item self-reported questionnaire that assesses the severity of depressive symptoms.

  2. Mood Disorder Questionnaire (MDQ)

    Time frame: At initial assessment

    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.

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

    Time frame: At initial assessment

    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.

  4. Reported Distress

    Time frame: After initial assessment

    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).

Sponsors and collaborators

Lead sponsor

Psyrin Inc.

Industry

Collaborators

  • Allwell Behavioral Health Services
  • The Brookline Center

Registry information

Official study 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

Acronym: MINDAIM

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jan 24, 2025
Registry last updated
Sep 3, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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