NCT06792175

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

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

• Status: Enrolling By Invitation
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Psyrin Inc. · Industry
• Sex: All
• Age: 13 Years – 60 Years
• Healthy volunteers: —

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)

Interventions

Type	Name	Description
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.
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.

Timeline

• Start date: 2025-02-04
• Primary completion: 2026-02-01
• Completion: 2026-07-01
• First posted: 2025-01-24
• Last updated: 2025-09-03

Locations

2 sites across 1 country: United States

Regulatory

• FDA-regulated device study