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

Part of paid clinical trials in Brookline, Massachusetts.

Sponsor
Psyrin Inc.
Study ID
NCT06792175
Status
Enrolling By Invitation

Conditions

Eligibility Criteria

Sex
ALL
Age
13 Years - 60 Years
Healthy Volunteers
Not accepted

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.
  • 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.

Study Details

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.

Key Dates

Start date
Feb 4, 2025
Status verified
Feb 2025
Primary completion
Feb 28, 2026
Completion
Jul 31, 2026

Study Design

Enrollment
500 participants (estimated)

Arms

  • Arm: Solicue (Any Mental Health Disorder)
    Any participant enrolled in the study and not part of additional analysis group.
  • Arm: Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder)
    Any participant enrolled in the study and exhibiting depressive symptoms as measured by PHQ-9 score.

Primary Outcome Measure

Speech Battery ("PSY-10") audio [ Time Frame: At initial assessment ]

Locations (2)

FacilityCityStateZIPSite coordinators
The Brookline CenterBrooklineMassachusetts02445-
Allwell Behavioral Health ServicesZanesvilleOhio43701-

Find similar trials in Brookline, MA

By condition
Autism spectrum disorderPTSD (post-traumatic stress disorder)
By specialty
PediatricsPsychiatryMental healthBehavioral health
By research site
The Brookline Center· Brookline, MAAllwell Behavioral Health Services· Zanesville, OH

Related Studies