Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Allwell Behavioral Health Services | UNKNOWN |
| The Brookline Center | UNKNOWN |
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Solicue (Any Mental Health Disorder) | Any participant enrolled in the study and not part of additional analysis group. |
| |
| Solicue & Mercuria (Bipolar Disorder & Major Depressive Disorder) | Any participant enrolled in the study and exhibiting depressive symptoms as measured by PHQ-9 score. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| 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 |
| 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) |
Not provided
Inclusion Criteria
Exclusion Criteria
Not provided
Not provided
Not provided
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.
Not provided
| Name | Affiliation | Role |
|---|---|---|
| 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 |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Brookline Center | Brookline | Massachusetts | 02445 | United States | ||
| Allwell Behavioral Health Services |
Not provided
| Label | URL |
|---|---|
| Study website | View source |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Audio of speech collected
|
|
| 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. |
|
|
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 |
| Zanesville |
| Ohio |
| 43701 |
| United States |
| ID | Term |
|---|---|
| D000067877 | Autism Spectrum Disorder |
| D000098647 | Generalized Anxiety Disorder |
| D001714 | Bipolar Disorder |
| D001289 | Attention Deficit Disorder with Hyperactivity |
| D013313 | Stress Disorders, Post-Traumatic |
| D009771 | Obsessive-Compulsive Disorder |
| D000092862 | Psychological Well-Being |
| ID | Term |
|---|---|
| D002659 | Child Development Disorders, Pervasive |
| D065886 | Neurodevelopmental Disorders |
| D001523 | Mental Disorders |
| D001008 | Anxiety Disorders |
| D000068105 | Bipolar and Related Disorders |
| D019964 | Mood Disorders |
| D019958 | Attention Deficit and Disruptive Behavior Disorders |
| D040921 | Stress Disorders, Traumatic |
| D000068099 | Trauma and Stressor Related Disorders |
| D010549 | Personal Satisfaction |
| D001519 | Behavior |
Not provided
Not provided