Cardiovascular, Depression Disorders, Fatigue Symptom
Conditions
Keywords
Fatigue, Depression, Artificial Intelligence
Brief summary
This 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.
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 clinical interventions will be provided.
Interventions
DIAGNOSTIC_TESTAI-enabled Okaya platform
AI-based conversational assessment using facial and vocal features to evaluate fatigue and depression.
Sponsors
Brijesh Patel
SmartTec Inc
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Patients referred to the cardiology clinic with acute or chronic issues, or hospitalized for acute cardiology issues will be enrolled. Inclusion Criteria: * Age ≥18, English-speaking, able to consent
Exclusion criteria
* Active substance use, nonverbal, cognitive disability, active suicidal/homicidal ideation * English is not primary language or requires a translator
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between Okaya (AI based) score and FAS | Baseline | Regression analysis comparing Okaya scores to FAS |
| Correlation between Okaya (AI) based score and PHQ-9 and FAS | Baseline | Regression analysis comparing Okaya scores to standard assessments |
| Correlation between Okaya (AI based) score and PHQ-9 | Baseline | Regression analysis comparing Okaya scores to PHQ-9 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Usability and patient satisfaction | Baseline | Ease-of-use ratings on the Okaya platform |
Countries
United States
Outcome results
None listed