Efficacy of a Novel Web-based Fatigue and Cognitive Assessment Platform in Detecting Fatigue and Depression
Brijesh Patel
Summary
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.
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 F…
Eligibility
- Age range
- 18–99 years
- Sex
- All
- Healthy volunteers
- Not specified
Inclusion Criteria: * Age ≥18, English-speaking, able to consent Exclusion Criteria: * Active substance use, nonverbal, cognitive disability, active suicidal/homicidal ideation
Interventions
- Diagnostic TestParticipants will complete PHQ-9, FAS, and Okaya assessments.
AI-based conversational assessment using facial and vocal features to evaluate fatigue and depression.
Locations (2)
- Indiana UniversityIndianapolis, Indiana
- Methodist HospitalIndianapolis, Indiana