AI Platform for Fatigue and Depression Detection
Purpose
This observational study evaluates the accuracy of the Okaya AI platform in detecting fatigue and depression in cardiology patients, comparing its assessments to PHQ-9 and Fatigue Assessment Scale scores.
Conditions
- Depression Disorders
- Fatigue Symptom
- Cardiovascular
Eligibility
- Eligible Ages
- Between 18 Years and 99 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- Age ≥18, English-speaking, able to consent
Exclusion Criteria
- Active substance use, nonverbal, cognitive disability, active suicidal/homicidal ideation
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Cross-Sectional
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
| Single Group Assignment | Participants will complete PHQ-9, FAS, and Okaya assessments. |
|
Recruiting Locations
More Details
- Status
- Recruiting
- Sponsor
- Brijesh Patel
Detailed Description
Patients frequently experience fatigue and depression, which are often underdiagnosed due to limitations in traditional screening tools. This study introduces the Okaya platform, a browser-based AI system that analyzes facial and vocal biomarkers collected during conversational check-ins. The platform uses computer vision and natural language processing to extract features such as eye contact, facial affect, pitch, volume, and speech patterns. These features are processed through regression models to generate a composite AI based score. The study aims to validate this score against PHQ-9 and FAS assessments. Participants will complete a single baseline check-in using the Okaya platform and complete standard questionnaires. No interventions will be provided.
