Anxiety, Depression, Healthy
Conditions
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.
Interventions
None listed
Sponsors
Aiberry, Inc
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
13 Years to 79 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Prediction of depression score from a self-report instrument | Single assessment | Error when predicting depression severity (none, mild, moderate, severe) based on QIDS-SR-16. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Classification of moderate-severe depression | Single assessment | Accuracy, sensitivity, specificity, and positive and negative predictive value when classifying whether participants meet established cut-off criterion for suspected MDD (QIDS \> 10) |
| Classification of moderate-severe anxiety | Single assessment | Accuracy, sensitivity, specificity, and positive and negative predictive value when classifying whether participants meet established cut-off criterion for suspected GAD (GAD-7 \> 9) |
| Prediction of anxiety score from a self-report instrument | Single assessment | Error when predicting anxiety severity (none, mild, moderate, severe) based on GAD-7. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Prediction of specific anxiety and depression symptoms | Single assessment | Error when predicting responses to individual items on the QIDS-SR or GAD-7. |
Countries
United States
Outcome results
None listed