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

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Attention Deficit Hyperactivity Disorder (ADHD); Autism Spectrum Disorder; Post Traumatic Stress Disorder; Obsessive Compulsive Disorder (OCD); Bipolar Disorder (BD); Depression - Major Depressive Disorder; Anxiety, Generalized; Schizophrenia Spectrum &Amp; Other Psychotic Disorders
• Intervention / Treatment: Diagnostic test: Solicue Machine Learning Models; Diagnostic test: Mercuria Machine Learning Models

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

Study Locations

• United States
  • Massachusetts
    • Brookline, Massachusetts, United States, 02445
      • The Brookline Center
  • Ohio
    • Zanesville, Ohio, United States, 43701
      • Allwell Behavioral Health Services

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria

1. Participants aged between 16 and 60 years.
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

1. Individuals experiencing acute mental health crises or severe symptoms that would preclude meaningful participation in the study, including acute intoxication.
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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Solicue (Any Mental Health Disorder); Any participant enrolled in the study and not part of additional analysis group.	Diagnostic test: 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.; Other Names: Solicue; Psyrin Speech Analysis; Solicue Artificial Intelligence
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; 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; Diagnostic test: 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 Names: Mercuria; Mercuria Artificial Intelligence

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient Health Questionnaire-9 (PHQ-9)	The PHQ-9 is a 9-item self-reported questionnaire that assesses the severity of depressive symptoms.	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.	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.	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).	After initial assessment

Collaborators and Investigators

• Sponsor: Psyrin Inc.
• Collaborators: Allwell Behavioral Health Services; The Brookline Center
• Investigators: Principal Investigator: 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.

Publications and helpful links

Helpful Links

• Study website

Study record dates

Study Major Dates

• Study Start (Actual): February 4, 2025
• Primary Completion (Estimated): February 1, 2026
• Study Completion (Estimated): July 1, 2026

Study Registration Dates

• First Submitted: January 19, 2025
• First Submitted That Met QC Criteria: January 19, 2025
• First Posted (Actual): January 24, 2025

Study Record Updates

• Last Update Posted (Estimated): September 3, 2025
• Last Update Submitted That Met QC Criteria: August 26, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Keywords: Mental Health; Artificial Intelligence; Natural Language Processing; Machine Learning; Acoustic Analysis; Acoustic Biomarkers; Clinical Testing; Vocal Biomarkers; Speech Analysis
• Additional Relevant MeSH Terms: Trauma and Stressor Related Disorders; Bipolar and Related Disorders; Mental Disorders; Mood Disorders; Neurodevelopmental Disorders; Child Development Disorders, Pervasive; Attention Deficit and Disruptive Behavior Disorders; Anxiety Disorders; Stress Disorders, Traumatic; Behavior; Personal Satisfaction; Autism Spectrum Disorder; Bipolar Disorder; Attention Deficit Disorder with Hyperactivity; Stress Disorders, Post-Traumatic; Obsessive-Compulsive Disorder; Psychological Well-Being; Generalized Anxiety Disorder
• Other Study ID Numbers: PSYRIN-0004

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No