Aiberry AI Mental Health Screening Platform

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

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.

Study Overview

• Status: Terminated
• Conditions: Depression; Healthy; Anxiety
• Intervention / Treatment: Other: Aiberry Depression Detection AI Platform

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.

• Study Type: Observational
• Enrollment (Actual): 800

Contacts and Locations

• Study Contact: Name: Clinical Study Manager; Phone Number: (512) 827-9734; Email: jocelyn.labrada@aiberry.com

Study Locations

• United States
  • Arizona
    • Tucson, Arizona, United States, 85719
      • University of Arizona
  • District of Columbia
    • Washington, District of Columbia, United States, 20057
      • Georgetown University
  • New York
    • New York, New York, United States, 10022
      • Hapworth Research Inc.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 13 years to 79 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Individuals with or without prior mental health diagnosis.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Interview & Depression Screening	Other: Aiberry Depression Detection AI Platform; Aiberry is a next-generation artificial intelligence (AI) company that leverages deep tech to screen for mental health and brain-related disorders.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
Prediction of anxiety score from a self-report instrument	Error when predicting anxiety severity (none, mild, moderate, severe) based on GAD-7.	Single assessment

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prediction of specific anxiety and depression symptoms	Error when predicting responses to individual items on the QIDS-SR or GAD-7.	Single assessment

Collaborators and Investigators

• Sponsor: Aiberry, Inc

Study record dates

Study Major Dates

• Study Start (Actual): September 22, 2022
• Primary Completion (Actual): April 6, 2023
• Study Completion (Actual): April 6, 2023

Study Registration Dates

• First Submitted: July 30, 2021
• First Submitted That Met QC Criteria: July 30, 2021
• First Posted (Actual): August 6, 2021

Study Record Updates

• Last Update Posted (Actual): June 9, 2023
• Last Update Submitted That Met QC Criteria: June 8, 2023
• Last Verified: February 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Behavioral Symptoms; Depression
• Other Study ID Numbers: Aiberry

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No