Clinical trial NCT04994899: Aiberry AI Mental Health Screening Platform
Official title: Development of the Aiberry AI Mental Health Screening Platform in a Diverse Population
Study type: OBSERVATIONAL
Overall status: TERMINATED
Conditions: Depression
Anxiety
Healthy
Lead sponsor: Aiberry, Inc
Sponsor class: INDUSTRY
First posted: 2021-08-06
Enrollment: 800
Sex: ALL
Minimum age (years): 13
Maximum age (years): 79
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals with or without prior mental health diagnosis.

Brief summary: Aiberry is creating a multi-modal artificial intelligence (AI) platform that analyzes facial, audio and text features to screen for mental illness. This multicenter study will be used to collect data to validate the platform's ability to detect depression and anxiety in a diverse patient population.

Detailed description: Aiberry is developing a proprietary AI platform that leverages machine learning to screen for mental illness using a multi-modal approach that fuses rich visual, audio, text, gestures, and eye gaze information. Our objective in this study is to validate algorithms that predict how individuals will respond to self-report questionnaires used to screen for major depressive disorder (MDD) and general anxiety disorder (GAD). Prior to enrollment, participants will participate in a baseline screening questionnaire to collect demographic information, health history, and current depression severity. If participants meet eligibility requirements and demographic recruitment targets, they will be invited to a single virtual study visit in which they will complete a 10-15 minute recorded interview with a study staff member along with three brief self-report questionnaires: 1) the Quick Inventory of Depression Symptoms Self Report (QIDS-SR-16), 2) the General Anxiety Disorder (GAD-7), and 3) the mini-version of the Mood and Anxiety Questionnaire (mini-MASQ), which will be used as a validity check that participant responses are consistent between this and the previous two questionnaires. We will evaluate how well the AI technology is able to predict self-reported symptoms of depression and anxiety by analyzing facial, audio, and text features from interview videos.

Eligibility criteria: Inclusion Criteria:

* Age 13 to 79 years old
* English-speaking

Exclusion Criteria:

* Previous participant in the study, within the last three months
* Individuals unable to verbally respond to standardized questions
* Individuals unable to participate in a virtual visit as they do not have the right hardware (phone/laptop) or no internet connectivity
* Individuals unable to provide consent

Primary outcomes:
measure: Prediction of depression score from a self-report instrument
timeFrame: Single assessment
Secondary outcomes:
measure: Classification of moderate-severe depression
timeFrame: Single assessment
measure: Classification of moderate-severe anxiety
timeFrame: Single assessment
measure: Prediction of anxiety score from a self-report instrument
timeFrame: Single assessment
Other outcomes:
measure: Prediction of specific anxiety and depression symptoms
timeFrame: Single assessment
Locations:
city: Tucson
country: United States
facility: University of Arizona
state: Arizona
city: Washington
country: United States
facility: Georgetown University
state: District of Columbia
city: New York
country: United States
facility: Hapworth Research Inc.
state: New York
Study officials:
References: