NCT07218263

AI Platform for Fatigue and Depression Detection

Efficacy of a Novel Web-based Fatigue and Cognitive Assessment Platform in Detecting Fatigue and Depression

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 100 (estimated)
• Sponsor: Brijesh Patel · Academic / Other
• Sex: All
• Age: 18 Years – 99 Years
• Healthy volunteers: —

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.

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.

Conditions

• Depression Disorders
• Fatigue Symptom
• Cardiovascular

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Participants will complete PHQ-9, FAS, and Okaya assessments.	AI-based conversational assessment using facial and vocal features to evaluate fatigue and depression.

Timeline

• Start date: 2025-12-01
• Primary completion: 2026-05-01
• Completion: 2026-05-01
• First posted: 2025-10-20
• Last updated: 2026-02-25

Locations

2 sites across 1 country: United States