Aiberry AI Mental Health Screening Platform

Healthy Clinical Trial

Official title:

Development of the Aiberry AI Mental Health Screening Platform in a Diverse Population

Clinical Trial Details — Status: Terminated

Administrative data

• NCT number: NCT04994899
• Other study ID #: Aiberry
• Status: Terminated
• Start date: September 22, 2022
• Est. completion date: April 6, 2023

Study information

• Verified date: February 2023
• Source: Aiberry, Inc
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Terminated
• Enrollment: 800
• Est. completion date: April 6, 2023
• Est. primary completion date: April 6, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 13 Years to 79 Years

Eligibility

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

Related Conditions & MeSH terms

• Anxiety
• Depression
• Healthy

Locations

Country	Name	City	State
United States	Hapworth Research Inc.	New York	New York
United States	University of Arizona	Tucson	Arizona
United States	Georgetown University	Washington	District of Columbia

Sponsors (1)

Lead Sponsor	Collaborator
Aiberry, Inc

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Prediction of specific anxiety and depression symptoms	Error when predicting responses to individual items on the QIDS-SR or GAD-7.	Single assessment
Primary	Prediction of depression score from a self-report instrument	Error when predicting depression severity (none, mild, moderate, severe) based on QIDS-SR-16.	Single assessment
Secondary	Classification of moderate-severe depression	Accuracy, sensitivity, specificity, and positive and negative predictive value when classifying whether participants meet established cut-off criterion for suspected MDD (QIDS > 10)	Single assessment
Secondary	Classification of moderate-severe anxiety	Accuracy, sensitivity, specificity, and positive and negative predictive value when classifying whether participants meet established cut-off criterion for suspected GAD (GAD-7 > 9)	Single assessment
Secondary	Prediction of anxiety score from a self-report instrument	Error when predicting anxiety severity (none, mild, moderate, severe) based on GAD-7.	Single assessment