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Набор по приглашению NCT06792175

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

— Наблюдательное Autism Spectrum Disorder Depression - Major Depressive Disorder Anxiety, Generalized Bipolar Disorder (BD)

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Solicue Machine Learning Models, Mercuria Machine Learning Models.
Кому может быть актуально
Состояния в реестре: Autism Spectrum Disorder, Depression - Major Depressive Disorder, Anxiety, Generalized, Bipolar Disorder (BD). Базовые параметры: 13 лет — 60 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
США
Следующий шаг
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Официальное название

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

Обзор

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.

Вмешательства

  • Диагностический тест 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, Soli
  • Диагностический тест 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 treatme

Первичные конечные точки

  • Speech Battery ("PSY-10") audio [Срок оценки: At initial assessment]
  • Clinical diagnosis [Срок оценки: 0 months, 3 months, 6 months]
  • Performance of AI models [Срок оценки: 0 months, 3 months, 6 months]
Вторичные конечные точки (4)
  • Patient Health Questionnaire-9 (PHQ-9) [Срок оценки: At initial assessment]
  • Mood Disorder Questionnaire (MDQ) [Срок оценки: At initial assessment]
  • DSM-5 Level 1 Cross-Cutting Symptom Measure (DSM-XC) [Срок оценки: At initial assessment]
  • Reported Distress [Срок оценки: After initial assessment]

Критерии участия

Критерии включения

  • 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.

Критерии исключения

  • 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

США · 2 центра
  • The Brookline Center — Brookline
  • Allwell Behavioral Health Services — Zanesville

Идентификаторы

NCT: NCT06792175 · PSYRIN-0004

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗